From 7033cac5904b997249d336fa3f0916edebb67b17 Mon Sep 17 00:00:00 2001 From: SeanAdams10 Date: Sun, 5 Apr 2026 07:22:38 -0400 Subject: [PATCH 1/2] Upgrade all dependencies to latest stable versions and modernize test framework - Update Python requirement from 3.10 to >=3.13 - Upgrade all packages to latest stable versions across pyproject.toml, setup.py, server/requirements.txt, and Dockerfile (python:3.13-bookworm) - Remove numpy<2 constraint, now using numpy 2.4.4 - Update tensorflow 2.19->2.21, ray 2.2->2.54.1, setuptools 77->82, and all other pinned dependencies - Replace legacy `import gym` with `import gymnasium` in rllib.py and remove deprecated `gym` package from dependencies - Fix Gymnasium env warnings: change observation_space from Box to Tuple(Box, Box), return obs tuples from reset()/step() instead of dicts, move metadata to info dict - Add 14 new pytest tests (test_gym_env_warnings.py) verifying no gymnasium warnings and correct observation format - Convert all 9 test files from unittest to pytest (testing/ and src/human_aware_rl/), removing all unittest.TestCase inheritance, setUp/tearDown -> fixtures, self.assert* -> plain assert - Fix flaky test_mdp_serialization by using tempfile instead of shared path, and fix test_render_state_from_dict misnamed helper function - Regenerate uv.lock with all updated dependencies BREAKING CHANGES: - Python 3.10/3.11/3.12 no longer supported (now requires >=3.13) - numpy 2.x: code using numpy 1.x-only APIs (e.g. removed type aliases like np.bool, np.int, np.float, np.object, np.str) will break. Pickled data using old numpy dtypes may trigger deprecation warnings. - Gymnasium env API change: Overcooked.reset() and step() now return observation as a tuple of two numpy arrays instead of a dict with "both_agent_obs"/"overcooked_state"/"other_agent_env_idx" keys. "overcooked_state" and "other_agent_env_idx" are now in the info dict. observation_space is now Tuple(Box, Box) instead of Box. - gym package removed: code importing `gym` must switch to `gymnasium` - Flask upgraded 2.x->3.x and Flask-SocketIO 4.x->5.x in server requirements (different async handling, python-socketio 4.x->5.x) - protobuf upgraded 3.x->7.x in server requirements (breaking API changes across major versions) - Dockerfile base image changed from python:3.7-buster to python:3.13-bookworm Co-Authored-By: Claude Opus 4.6 (1M context) --- pyproject.toml | 35 +- setup.py | 44 +- src/human_aware_rl/human/tests.py | 64 +- .../imitation/behavior_cloning_tf2_test.py | 98 +- src/human_aware_rl/ppo/ppo_rllib_test.py | 153 +- src/human_aware_rl/rllib/rllib.py | 2 +- src/human_aware_rl/rllib/tests.py | 60 +- .../testing/test_mdp_serialization/dummy.json | 2 +- src/overcooked_ai_py/mdp/overcooked_env.py | 48 +- src/overcooked_demo/server/Dockerfile | 2 +- src/overcooked_demo/server/requirements.txt | 35 +- testing/agent_test.py | 174 +- testing/mdp_gen_schedule_test.py | 113 +- testing/overcooked_test.py | 639 +++--- testing/planners_test.py | 824 ++------ testing/test_gym_env_warnings.py | 125 ++ testing/visualization_test.py | 84 +- uv.lock | 1830 +++++++++++------ 18 files changed, 2162 insertions(+), 2170 deletions(-) create mode 100644 testing/test_gym_env_warnings.py diff --git a/pyproject.toml b/pyproject.toml index 02908bdd..fbf73f5a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,5 +1,5 @@ [build-system] -requires = ["setuptools>=77.0.3"] +requires = ["setuptools>=82.0.1"] build-backend = "setuptools.build_meta" [project] @@ -7,32 +7,31 @@ name = "overcooked_ai" version = "2.0.0" description = "Cooperative multi-agent environment based on Overcooked" readme = "README.md" -requires-python = ">=3.10,<3.11" +requires-python = ">=3.13" license = { file = "LICENSE" } authors = [{ name = "Micah Carroll", email = "mdc@berkeley.edu" }] urls = { homepage = "https://github.com/HumanCompatibleAI/overcooked_ai" } keywords = ["Overcooked", "AI", "Reinforcement Learning"] dependencies = [ - "dill", - "gymnasium", - "ipywidgets", - "numpy<2", - "opencv-python", - "pygame", - "scipy", - "setuptools>=77.0.3", - "tqdm", + "dill>=0.4.1", + "gymnasium>=1.2.2", + "ipywidgets>=8.1.8", + "numpy>=2.4.4", + "opencv-python>=4.13.0.92", + "pygame>=2.6.1", + "scipy>=1.17.1", + "setuptools>=82.0.1", + "tqdm>=4.67.3", ] [project.optional-dependencies] harl = [ - "ipykernel", - "ray[rllib,tune]==2.2", - "dm-tree", - "lz4", - "tensorflow[and-cuda]==2.19.0", - "wandb", - "gym", + "ipykernel>=7.2.0", + "ray[rllib,tune]>=2.54.1", + "dm-tree>=0.1.10", + "lz4>=4.4.5", + "tensorflow[and-cuda]>=2.21.0", + "wandb>=0.25.1", ] [project.scripts] diff --git a/setup.py b/setup.py index 5a5f0a77..e2f44b21 100644 --- a/setup.py +++ b/setup.py @@ -38,32 +38,32 @@ ], }, install_requires=[ - "dill", - "numpy<2.0.0", - "scipy", - "tqdm", - "gymnasium", - "ipython", - "pygame", - "ipywidgets", - "opencv-python", - "flask", - "flask-socketio", + "dill>=0.4.1", + "numpy>=2.4.4", + "scipy>=1.17.1", + "tqdm>=4.67.3", + "gymnasium>=1.2.2", + "ipython>=9.12.0", + "pygame>=2.6.1", + "ipywidgets>=8.1.8", + "opencv-python>=4.13.0.92", + "flask>=3.1.3", + "flask-socketio>=5.6.1", ], # removed overlapping dependencies extras_require={ "harl": [ - "wandb", - "GitPython", - "memory_profiler", - "sacred", - "pymongo", - "matplotlib", - "requests", - "seaborn==0.9.0", - "ray[rllib]>=2.5.0", - "protobuf", - "tensorflow>=2.14.0", + "wandb>=0.25.1", + "GitPython>=3.1.46", + "memory_profiler>=0.61.0", + "sacred>=0.8.7", + "pymongo>=4.16.0", + "matplotlib>=3.10.8", + "requests>=2.33.1", + "seaborn>=0.13.2", + "ray[rllib]>=2.54.1", + "protobuf>=7.34.1", + "tensorflow>=2.21.0", ] }, entry_points={ diff --git a/src/human_aware_rl/human/tests.py b/src/human_aware_rl/human/tests.py index 9870d8e1..7ac5b196 100644 --- a/src/human_aware_rl/human/tests.py +++ b/src/human_aware_rl/human/tests.py @@ -2,11 +2,9 @@ import os import pickle import shutil -import sys -import unittest import numpy as np -from numpy.testing._private.utils import assert_raises +import pytest from human_aware_rl.human.process_dataframes import ( csv_to_df_pickle, @@ -29,7 +27,7 @@ ) -class TestProcessDataFrames(unittest.TestCase): +class TestProcessDataFrames: temp_data_dir = "this_is_a_temp" data_len_2019 = 3546 data_len_2020 = 1189 @@ -51,16 +49,11 @@ class TestProcessDataFrames(unittest.TestCase): "layouts": ["cramped_room"], } - def setUp(self): - print( - "\nIn Class {}, in Method {}".format( - self.__class__.__name__, self._testMethodName - ) - ) + @pytest.fixture(autouse=True) + def _setup(self): if not os.path.exists(self.temp_data_dir): os.makedirs(self.temp_data_dir) - - def tearDown(self): + yield shutil.rmtree(self.temp_data_dir) def test_csv_to_df_pickle_2019(self): @@ -68,44 +61,41 @@ def test_csv_to_df_pickle_2019(self): params["csv_path"] = DUMMY_2019_RAW_HUMAN_DATA_PATH params["button_presses_threshold"] = 0.0 data = csv_to_df_pickle(**params) - self.assertEqual(len(data), self.data_len_2019) + assert len(data) == self.data_len_2019 params = copy.deepcopy(self.base_csv_to_df_params) params["csv_path"] = DUMMY_2019_RAW_HUMAN_DATA_PATH params["button_presses_threshold"] = 0.7 data = csv_to_df_pickle(**params) - self.assertLess(len(data), self.data_len_2019) + assert len(data) < self.data_len_2019 def test_csv_to_df_pickle_2020(self): params = copy.deepcopy(self.base_csv_to_df_params) params["button_presses_threshold"] = 0.0 data = csv_to_df_pickle(**params) - self.assertEqual(len(data), self.data_len_2020) + assert len(data) == self.data_len_2020 params = copy.deepcopy(self.base_csv_to_df_params) params["button_presses_threshold"] = 0.7 data = csv_to_df_pickle(**params) - self.assertLess(len(data), self.data_len_2020) + assert len(data) < self.data_len_2020 def test_csv_to_df_pickle(self): - # Try various button thresholds (hand-picked to lie between different values for dummy data games) button_thresholds = [0.2, 0.6, 0.7] lengths = [] for threshold in button_thresholds: - # dummy dataset is too small to partion so we set train_test_split=False params = copy.deepcopy(self.base_csv_to_df_params) params["button_presses_threshold"] = threshold data = csv_to_df_pickle(**params) lengths.append(len(data)) - # Filtered data size should be monotonically decreasing wrt button_threshold for i in range(len(lengths) - 1): - self.assertGreaterEqual(lengths[i], lengths[i + 1]) + assert lengths[i] >= lengths[i + 1] - # Picking a threshold that's suficiently high discards all data, should result in value error params = copy.deepcopy(self.base_csv_to_df_params) params["button_presses_threshold"] = 0.8 - self.assertRaises(ValueError, csv_to_df_pickle, **params) + with pytest.raises(ValueError): + csv_to_df_pickle(**params) def test_get_trajs_from_data_2019(self): params = copy.deepcopy(self.base_get_trajs_from_data_params) @@ -117,14 +107,12 @@ def test_get_trajs_from_data_2019_featurize(self): trajectories, _ = get_trajs_from_data(**params) def test_get_trajs_from_data_2020(self): - # Ensure we can properly deserialize states with updated objects (i.e tomatoes) params = copy.deepcopy(self.base_get_trajs_from_data_params) params["layouts"] = ["inverse_marshmallow_experiment"] params["data_path"] = DUMMY_2020_CLEAN_HUMAN_DATA_PATH trajectories, _ = get_trajs_from_data(**params) def test_get_trajs_from_data_2020_featurize(self): - # Ensure we can properly featurize states with updated dynamics and updated objects (i.e tomatoes) params = copy.deepcopy(self.base_get_trajs_from_data_params) params["layouts"] = ["inverse_marshmallow_experiment"] params["data_path"] = DUMMY_2020_CLEAN_HUMAN_DATA_PATH @@ -132,7 +120,6 @@ def test_get_trajs_from_data_2020_featurize(self): trajectories, _ = get_trajs_from_data(**params) def test_csv_to_df_to_trajs_integration(self): - # Ensure the output of 'csv_to_df_pickle' works as valid input to 'get_trajs_from_data' params = copy.deepcopy(self.base_csv_to_df_params) _ = csv_to_df_pickle(**params) @@ -144,7 +131,7 @@ def test_csv_to_df_to_trajs_integration(self): _ = get_trajs_from_data(**params) -class TestHumanDataConversion(unittest.TestCase): +class TestHumanDataConversion: temp_dir = "this_is_also_a_temp" infile = DUMMY_2019_CLEAN_HUMAN_DATA_PATH horizon = 400 @@ -160,12 +147,8 @@ def _equal_pickle_and_env_state_dict( ["timestep", "all_orders", "bonus_orders"], ) - def setUp(self): - print( - "\nIn Class {}, in Method {}".format( - self.__class__.__name__, self._testMethodName - ) - ) + @pytest.fixture(autouse=True) + def _setup(self): if not os.path.exists(self.temp_dir): os.makedirs(self.temp_dir) @@ -191,7 +174,7 @@ def setUp(self): with open(outfile, "rb") as f: self.human_data = pickle.load(f)[self.layout_name] - def tearDown(self): + yield shutil.rmtree(self.temp_dir) def test_state(self): @@ -200,17 +183,10 @@ def test_state(self): if state_dict.items() == self.starting_state_dict.items(): self.env.reset() else: - self.assertTrue( - self._equal_pickle_and_env_state_dict( - state_dict, self.env.state.to_dict() - ), - "Expected state:\t\n{}\n\nActual state:\t\n{}".format( - self.env.state.to_dict(), state_dict - ), + assert self._equal_pickle_and_env_state_dict( + state_dict, self.env.state.to_dict() + ), "Expected state:\t\n{}\n\nActual state:\t\n{}".format( + self.env.state.to_dict(), state_dict ) self.env.step(joint_action=joint_action) idx += 1 - - -if __name__ == "__main__": - unittest.main() diff --git a/src/human_aware_rl/imitation/behavior_cloning_tf2_test.py b/src/human_aware_rl/imitation/behavior_cloning_tf2_test.py index 37f04493..800d4067 100644 --- a/src/human_aware_rl/imitation/behavior_cloning_tf2_test.py +++ b/src/human_aware_rl/imitation/behavior_cloning_tf2_test.py @@ -1,14 +1,12 @@ -import argparse import gc import os import pickle import shutil -import sys import time -import unittest import warnings import numpy as np +import pytest import tensorflow as tf from human_aware_rl.human.process_dataframes import get_trajs_from_data @@ -33,37 +31,28 @@ def _clear_pickle(): pickle.dump({}, f) -class TestBCTraining(unittest.TestCase): +class TestBCTraining: """ - Unittests for behavior cloning training and utilities + Tests for behavior cloning training and utilities compute_pickle (bool): Whether the results of this test should be stored as the expected values for future tests strict (bool): Whether the results of this test should be compared against expected values for exact match - min_performance (int): Minimum reward achieved in BC-BC rollout after training to consider training successfull - - Note, this test always performs a basic sanity check to verify some learning is happening, even if the `strict` param is false + min_performance (int): Minimum reward achieved in BC-BC rollout after training to consider training successful """ - def __init__(self, test_name): - super(TestBCTraining, self).__init__(test_name) - self.compute_pickle = False - self.strict = False - self.min_performance = 0 + compute_pickle = False + strict = False + min_performance = 0 + + @pytest.fixture(autouse=True) + def _setup(self): assert not (self.compute_pickle and self.strict), ( "Cannot compute pickle and run strict reproducibility tests at same time" ) if self.compute_pickle: _clear_pickle() - def setUp(self): set_global_seed(0) - print( - "\nIn Class {}, in Method {}".format( - self.__class__.__name__, self._testMethodName - ) - ) - # unittest generates a lot of warning msgs due to third-party dependencies (e.g. ray[rllib] using outdated np methods) - # not a problem when directly ran, but when using -m unittest this helps filter out the warnings warnings.simplefilter("ignore", ResourceWarning) warnings.simplefilter("ignore", DeprecationWarning) self.bc_params = get_bc_params( @@ -87,22 +76,18 @@ def setUp(self): with open(BC_EXPECTED_DATA_PATH, "rb") as f: self.expected = pickle.load(f) - # Disable TF warnings and infos tf.get_logger().setLevel("ERROR") - def tearDown(self): + yield + if self.compute_pickle: with open(BC_EXPECTED_DATA_PATH, "wb") as f: pickle.dump(self.expected, f) - # Force garbage collection to close any open files gc.collect() - - # Add a small delay to ensure files are released time.sleep(0.1) try: - # Use ignore_errors=True to force removal even if some files are still locked shutil.rmtree(self.model_dir, ignore_errors=True) except Exception as e: print(f"Warning: Could not fully remove directory {self.model_dir}: {e}") @@ -113,21 +98,17 @@ def test_model_construction(self): if self.compute_pickle: self.expected["test_model_construction"] = model(self.dummy_input) if self.strict: - self.assertTrue( - np.allclose( - model(self.dummy_input), - self.expected["test_model_construction"], - ) + assert np.allclose( + model(self.dummy_input), + self.expected["test_model_construction"], ) def test_save_and_load(self): model = build_bc_model(**self.bc_params) save_bc_model(self.model_dir, model, self.bc_params) loaded_model, loaded_params = load_bc_model(self.model_dir) - self.assertDictEqual(self.bc_params, loaded_params) - self.assertTrue( - np.allclose(model(self.dummy_input), loaded_model(self.dummy_input)) - ) + assert self.bc_params == loaded_params + assert np.allclose(model(self.dummy_input), loaded_model(self.dummy_input)) def test_training(self): model = train_bc_model(self.model_dir, self.bc_params) @@ -135,26 +116,22 @@ def test_training(self): if self.compute_pickle: self.expected["test_training"] = model(self.dummy_input) if self.strict: - self.assertTrue( - np.allclose(model(self.dummy_input), self.expected["test_training"]) - ) + assert np.allclose(model(self.dummy_input), self.expected["test_training"]) def test_agent_evaluation(self): self.bc_params["training_params"]["epochs"] = 20 model = train_bc_model(self.model_dir, self.bc_params) results = evaluate_bc_model(model, self.bc_params) - # Sanity Check - self.assertGreaterEqual(results, self.min_performance) + assert results >= self.min_performance if self.compute_pickle: self.expected["test_agent_evaluation"] = results if self.strict: - self.assertAlmostEqual(results, self.expected["test_agent_evaluation"]) + assert results == pytest.approx(self.expected["test_agent_evaluation"]) class TestBCTrainingLSTM(TestBCTraining): - # LSTM tests break on older versions of tensorflow so be careful with this def test_lstm_construction(self): self.bc_params["use_lstm"] = True model = build_bc_model(**self.bc_params) @@ -162,11 +139,9 @@ def test_lstm_construction(self): if self.compute_pickle: self.expected["test_lstm_construction"] = model(self.dummy_input) if self.strict: - self.assertTrue( - np.allclose( - model(self.dummy_input), - self.expected["test_lstm_construction"], - ) + assert np.allclose( + model(self.dummy_input), + self.expected["test_lstm_construction"], ) def test_lstm_training(self): @@ -176,11 +151,9 @@ def test_lstm_training(self): if self.compute_pickle: self.expected["test_lstm_training"] = model(self.dummy_input) if self.strict: - self.assertTrue( - np.allclose( - model(self.dummy_input), - self.expected["test_lstm_training"], - ) + assert np.allclose( + model(self.dummy_input), + self.expected["test_lstm_training"], ) def test_lstm_evaluation(self): @@ -189,25 +162,22 @@ def test_lstm_evaluation(self): model = train_bc_model(self.model_dir, self.bc_params) results = evaluate_bc_model(model, self.bc_params) - # Sanity Check - self.assertGreaterEqual(results, self.min_performance) + assert results >= self.min_performance if self.compute_pickle: self.expected["test_lstm_evaluation"] = results if self.strict: - self.assertAlmostEqual(results, self.expected["test_lstm_evaluation"]) + assert results == pytest.approx(self.expected["test_lstm_evaluation"]) def test_lstm_save_and_load(self): self.bc_params["use_lstm"] = True model = build_bc_model(**self.bc_params) save_bc_model(self.model_dir, model, self.bc_params) loaded_model, loaded_params = load_bc_model(self.model_dir) - self.assertDictEqual(self.bc_params, loaded_params) - self.assertTrue( - np.allclose( - self._lstm_forward(model, self.dummy_input)[0], - self._lstm_forward(loaded_model, self.dummy_input)[0], - ) + assert self.bc_params == loaded_params + assert np.allclose( + self._lstm_forward(model, self.dummy_input)[0], + self._lstm_forward(loaded_model, self.dummy_input)[0], ) def _lstm_forward(self, model, obs_batch, states=None): @@ -218,7 +188,3 @@ def _lstm_forward(self, model, obs_batch, states=None): logits, states = model_out[0], model_out[1:] logits = logits.reshape((logits.shape[0], -1)) return logits, states - - -if __name__ == "__main__": - unittest.main() diff --git a/src/human_aware_rl/ppo/ppo_rllib_test.py b/src/human_aware_rl/ppo/ppo_rllib_test.py index 59021e6d..e7d317b9 100644 --- a/src/human_aware_rl/ppo/ppo_rllib_test.py +++ b/src/human_aware_rl/ppo/ppo_rllib_test.py @@ -3,9 +3,9 @@ import pickle import random import shutil -import unittest import warnings +import pytest import ray os.environ["RUN_ENV"] = "local" @@ -26,7 +26,6 @@ from overcooked_ai_py.mdp.overcooked_mdp import OvercookedGridworld -# Note: using the same seed across architectures can still result in differing values def set_global_seed(seed): random.seed(seed) np.random.seed(seed) @@ -35,85 +34,61 @@ def set_global_seed(seed): def _clear_pickle(): - # Write an empty dictionary to our static "expected" results location with open(PPO_EXPECTED_DATA_PATH, "wb") as f: pickle.dump({}, f) -class TestPPORllib(unittest.TestCase): - +class TestPPORllib: """ - Unittests for rllib PPO training loop + Tests for rllib PPO training loop compute_pickle (bool): Whether the results of this test should be stored as the expected values for future tests strict (bool): Whether the results of this test should be compared against expected values for exact match min_performance (int): Minimum sparse reward that must be achieved during training for test to count as "success" - - Note, this test always performs a basic sanity check to verify some learning is happening, even if the `strict` param is false """ - def __init__(self, test_name): - super(TestPPORllib, self).__init__(test_name) - # default parameters, feel free to change - self.compute_pickle = False - # Reproducibility test - self.strict = False - # this value exists as sanity checks since random actions can still obtain at least this much dense reward - # some performance thresholds are hardcoded and are determined by comparing empirical performances with and with actual trainings/gradient updates - self.min_performance = 5 + compute_pickle = False + strict = False + min_performance = 5 + + @pytest.fixture(autouse=True) + def _setup(self): assert not ( self.compute_pickle and self.strict ), "Cannot compute pickle and run strict reproducibility tests at same time" if self.compute_pickle: _clear_pickle() - def setUp(self): set_global_seed(0) - print( - "\nIn Class {}, in Method {}".format( - self.__class__.__name__, self._testMethodName - ) - ) - # unittest generates a lot of warning msgs due to third-party dependencies (e.g. ray[rllib] using outdated np methods) - # not a problem when directly ran, but when using -m unittest this helps filter out the warnings warnings.filterwarnings("ignore") - # Setting CWD os.chdir(os.path.dirname(os.path.abspath(__file__))) - # Temporary disk space to store logging results from tests self.temp_results_dir = os.path.join( os.path.abspath("."), "results_temp" ) self.temp_model_dir = os.path.join(os.path.abspath("."), "model_temp") - # Make all necessary directories if not os.path.exists(self.temp_model_dir): os.makedirs(self.temp_model_dir) if not os.path.exists(self.temp_results_dir): os.makedirs(self.temp_results_dir) - # Load in expected values (this is an empty dict if compute_pickle=True) with open(PPO_EXPECTED_DATA_PATH, "rb") as f: self.expected = pickle.load(f) - def tearDown(self): - # Write results of this test to disk for future reproducibility tests - # Note: This causes unit tests to have a side effect (generally frowned upon) and only works because - # unittest is single threaded. If tests were run concurrently this could result in a race condition! + yield + if self.compute_pickle: with open(PPO_EXPECTED_DATA_PATH, "wb") as f: pickle.dump(self.expected, f) - # Cleanup shutil.rmtree(self.temp_results_dir) shutil.rmtree(self.temp_model_dir) ray.shutdown() def test_save_load(self): - # Train a quick self play agent for 2 iterations ex.run( config_updates={ - # Please feel free to modify the parameters below "results_dir": self.temp_results_dir, "experiment_name": "save_load_test", "layout_name": "cramped_room", @@ -131,12 +106,8 @@ def test_save_load(self): options={"--loglevel": "ERROR"}, ) - # Kill all ray processes to ensure loading works in a vaccuum ray.shutdown() - # Where the agent is stored (this is kind of hardcoded, would like for it to be more easily obtainable) - # 2 checkpoints(checkpoint_000001 and checkpoint_000002) are saved - # since we are only interested in reproducing the same actions, either one should be fine load_path = os.path.join( glob.glob(os.path.join(self.temp_results_dir, "save_load_test*"))[ 0 @@ -144,36 +115,29 @@ def test_save_load(self): "checkpoint_000002", ) - # Load a dummy state mdp = OvercookedGridworld.from_layout_name("cramped_room") state = mdp.get_standard_start_state() - # Ensure simple single-agent loading works agent_0 = load_agent(load_path) agent_0.reset() agent_1 = load_agent(load_path) agent_1.reset() - # Ensure forward pass of policy network still works _, _ = agent_0.action(state) _, _ = agent_1.action(state) - # Now let's load an agent pair and evaluate it agent_pair = load_agent_pair(load_path) ae = AgentEvaluator.from_layout_name( mdp_params={"layout_name": "cramped_room"}, env_params={"horizon": 400}, ) - # We assume no runtime errors => success, no performance consistency check for now ae.evaluate_agent_pair(agent_pair, 1, info=False) def test_ppo_sp_no_phi(self): - # Train a self play agent for 20 iterations results = ex.run( config_updates={ - # Please feel free to modify the parameters below "results_dir": self.temp_results_dir, "num_workers": 2, "train_batch_size": 800, @@ -188,23 +152,16 @@ def test_ppo_sp_no_phi(self): }, options={"--loglevel": "ERROR"}, ).result - # Sanity check (make sure it begins to learn to receive dense reward) - self.assertGreaterEqual( - results["average_total_reward"], self.min_performance - ) + assert results["average_total_reward"] >= self.min_performance if self.compute_pickle: self.expected["test_ppo_sp_no_phi"] = results - - # Reproducibility test if self.strict: - self.assertDictEqual(results, self.expected["test_ppo_sp_no_phi"]) + assert results == self.expected["test_ppo_sp_no_phi"] def test_ppo_sp_yes_phi(self): - # Train a self play agent for 20 iterations results = ex.run( config_updates={ - # Please feel free to modify the parameters below "results_dir": self.temp_results_dir, "num_workers": 2, "train_batch_size": 1600, @@ -220,19 +177,14 @@ def test_ppo_sp_yes_phi(self): }, options={"--loglevel": "ERROR"}, ).result - # Sanity check (make sure it begins to learn to receive dense reward) - # This value is determined by comparing emperical performances with and without actual training updates - self.assertGreaterEqual(results["average_total_reward"], 13) + assert results["average_total_reward"] >= 13 if self.compute_pickle: self.expected["test_ppo_sp_yes_phi"] = results - - # Reproducibility test if self.strict: - self.assertDictEqual(results, self.expected["test_ppo_sp_yes_phi"]) + assert results == self.expected["test_ppo_sp_yes_phi"] def test_ppo_fp_sp_no_phi(self): - # Train a self play agent for 20 iterations results = ex_fp.run( config_updates={ "results_dir": self.temp_results_dir, @@ -252,20 +204,14 @@ def test_ppo_fp_sp_no_phi(self): }, options={"--loglevel": "ERROR"}, ).result - # Sanity check (make sure it begins to learn to receive dense reward) - self.assertGreaterEqual(results["average_total_reward"], 7) + assert results["average_total_reward"] >= 7 if self.compute_pickle: self.expected["test_ppo_fp_sp_no_phi"] = results - - # Reproducibility test if self.strict: - self.assertDictEqual( - results, self.expected["test_ppo_fp_sp_no_phi"] - ) + assert results == self.expected["test_ppo_fp_sp_no_phi"] def test_ppo_fp_sp_yes_phi(self): - # Train a self play agent for 20 iterations results = ex_fp.run( config_updates={ "results_dir": self.temp_results_dir, @@ -285,23 +231,14 @@ def test_ppo_fp_sp_yes_phi(self): }, options={"--loglevel": "ERROR"}, ).result - - # Sanity check (make sure it begins to learn to receive dense reward) - self.assertGreaterEqual( - results["average_total_reward"], self.min_performance - ) + assert results["average_total_reward"] >= self.min_performance if self.compute_pickle: self.expected["test_ppo_fp_sp_yes_phi"] = results - - # Reproducibility test if self.strict: - self.assertDictEqual( - results, self.expected["test_ppo_fp_sp_yes_phi"] - ) + assert results == self.expected["test_ppo_fp_sp_yes_phi"] def test_ppo_bc(self): - # Train bc model model_dir = self.temp_model_dir params_to_override = { "layouts": ["asymmetric_advantages_tomato"], @@ -311,7 +248,6 @@ def test_ppo_bc(self): bc_params = get_bc_params(**params_to_override) train_bc_model(model_dir, bc_params) - # Train rllib model config_updates = { "results_dir": self.temp_results_dir, "bc_schedule": [(0.0, 0.0), (8e3, 1.0)], @@ -324,56 +260,9 @@ def test_ppo_bc(self): results = ex.run( config_updates=config_updates, options={"--loglevel": "ERROR"} ).result - # Sanity check - # This value is determined by comparing emperical performances with and without actual training updates - self.assertGreaterEqual(results["average_total_reward"], 30) + assert results["average_total_reward"] >= 30 if self.compute_pickle: self.expected["test_ppo_bc"] = results - - # Reproducibility test if self.strict: - self.assertDictEqual(results, self.expected["test_ppo_bc"]) - - # def test_resume_functionality(self): - # load_path = os.path.join( - # os.path.abspath("."), - # "trained_example/checkpoint_000500", - # ) - # # Load and train an agent for another iteration - # results = ex_fp.run( - # config_updates={ - # "results_dir": self.temp_results_dir, - # "num_workers": 1, - # "num_training_iters": 1, - # "resume_checkpoint_path": load_path, - # "verbose": False, - # "evaluation_display": False, - # }, - # options={"--loglevel": "ERROR"}, - # ).result - - # # Test that the rewards from 1 additional iteration are not too different from the original model - # # performance - - # threshold = 0.1 - - # rewards = get_last_episode_rewards("trained_example/result.json") - - # # Test total reward - # self.assertAlmostEqual( - # rewards["episode_reward_mean"], - # results["average_total_reward"], - # delta=threshold * rewards["episode_reward_mean"], - # ) - # # Test sparse reward - # self.assertAlmostEqual( - # rewards["sparse_reward_mean"], - # results["average_sparse_reward"], - # delta=threshold * rewards["sparse_reward_mean"], - # ) - - -if __name__ == "__main__": - warnings.filterwarnings("ignore") - unittest.main() + assert results == self.expected["test_ppo_bc"] diff --git a/src/human_aware_rl/rllib/rllib.py b/src/human_aware_rl/rllib/rllib.py index f995f259..b5e50fa5 100644 --- a/src/human_aware_rl/rllib/rllib.py +++ b/src/human_aware_rl/rllib/rllib.py @@ -7,7 +7,7 @@ from pathlib import Path import dill -import gym as gymnasium # Things break with gymnasium because rllib can't handle it +import gymnasium import numpy as np import ray from ray.rllib.agents.ppo import PPOTrainer diff --git a/src/human_aware_rl/rllib/tests.py b/src/human_aware_rl/rllib/tests.py index 94a2c149..70bff176 100644 --- a/src/human_aware_rl/rllib/tests.py +++ b/src/human_aware_rl/rllib/tests.py @@ -1,8 +1,7 @@ import copy -import unittest -from math import isclose import numpy as np +import pytest from human_aware_rl.rllib.rllib import OvercookedMultiAgent from human_aware_rl.rllib.utils import ( @@ -12,22 +11,15 @@ ) -class RllibEnvTest(unittest.TestCase): - def setUp(self): - print( - "\nIn Class {}, in Method {}".format( - self.__class__.__name__, self._testMethodName - ) - ) +class RllibEnvTest: + @pytest.fixture(autouse=True) + def _setup(self): self.params = copy.deepcopy(OvercookedMultiAgent.DEFAULT_CONFIG) self.timesteps = [0, 10, 100, 500, 1000, 1500, 2000, 2500] - def tearDown(self): - pass - def _assert_lists_almost_equal(self, first, second, places=7): for a, b in zip(first, second): - self.assertAlmostEqual(a, b, places=places) + assert a == pytest.approx(b, abs=10**-places) def _test_bc_schedule(self, bc_schedule, expected_bc_factors): self.params["multi_agent_params"]["bc_schedule"] = bc_schedule @@ -48,16 +40,16 @@ def _test_bc_creation_proportion(self, env, factor, trials=10000): num_bc = sum( map(lambda agent: int(agent.startswith("bc")), env.curr_agents) ) - self.assertLessEqual(num_bc, 1) + assert num_bc <= 1 tot_bc += num_bc actual_factor = tot_bc / trials - self.assertAlmostEqual(actual_factor, factor, places=1) + assert actual_factor == pytest.approx(factor, abs=0.1) def test_env_creation(self): # Valid creation env = OvercookedMultiAgent.from_config(self.params) for param, expected in self.params["multi_agent_params"].items(): - self.assertEqual(expected, getattr(env, param)) + assert expected == getattr(env, param) # Invalid bc_schedules invalid_schedules = [ @@ -67,9 +59,8 @@ def test_env_creation(self): ] for sched in invalid_schedules: self.params["multi_agent_params"]["bc_schedule"] = sched - self.assertRaises( - AssertionError, OvercookedMultiAgent.from_config, self.params - ) + with pytest.raises(AssertionError): + OvercookedMultiAgent.from_config(self.params) def test_reward_shaping_annealing(self): self.params["multi_agent_params"]["reward_shaping_factor"] = 1 @@ -123,8 +114,8 @@ def test_agent_creation(self): obs = env.reset() # Check that we have the right number of agents with valid names - self.assertEqual(len(env.curr_agents), 2) - self.assertListEqual(list(obs.keys()), env.curr_agents) + assert len(env.curr_agents) == 2 + assert list(obs.keys()) == env.curr_agents # Ensure that bc agents are created 'factor' percentage of the time bc_factors = [0.0, 0.1, 0.5, 0.9, 1.0] @@ -132,18 +123,7 @@ def test_agent_creation(self): self._test_bc_creation_proportion(env, factor) -class RllibUtilsTest(unittest.TestCase): - def setUp(self): - print( - "\nIn Class {}, in Method {}".format( - self.__class__.__name__, self._testMethodName - ) - ) - pass - - def tearDown(self): - pass - +class RllibUtilsTest: def test_softmax(self): logits = np.array( [ @@ -164,18 +144,16 @@ def test_softmax(self): actual = softmax(logits) - self.assertTrue(np.allclose(expected, actual)) + assert np.allclose(expected, actual) def test_iterable_equal(self): a = [(1,), (1, 2)] b = ([1], [1, 2]) - - self.assertTrue(iterable_equal(a, b)) + assert iterable_equal(a, b) a = [(1, 2), (1)] b = [(1,), (1, 2)] - - self.assertFalse(iterable_equal(a, b)) + assert not iterable_equal(a, b) def test_get_required_arguments(self): def foo1(a): @@ -197,8 +175,4 @@ def foo5(a, b="bar", d="baz", **kwargs): expected = [1, 2, 3, 2, 1] for fn, expected in zip(fns, expected): - self.assertEqual(expected, len(get_required_arguments(fn))) - - -if __name__ == "__main__": - unittest.main() + assert expected == len(get_required_arguments(fn)) diff --git a/src/overcooked_ai_py/data/testing/test_mdp_serialization/dummy.json b/src/overcooked_ai_py/data/testing/test_mdp_serialization/dummy.json index 2bf67064..5ecd78e1 100644 --- a/src/overcooked_ai_py/data/testing/test_mdp_serialization/dummy.json +++ b/src/overcooked_ai_py/data/testing/test_mdp_serialization/dummy.json @@ -1 +1 @@ -{"players": [{"position": [3, 1], "orientation": [1, 0], "held_object": {"name": "onion", "position": [3, 1]}}, {"position": [2, 1], "orientation": [0, -1], "held_object": {"name": "onion", "position": [2, 1]}}], "objects": [{"name": "soup", "position": [1, 0], "_ingredients": [{"name": "tomato", "position": [1, 0]}], "cooking_tick": 2, "is_cooking": false, "is_ready": true, "is_idle": false, "cook_time": 2, "_cooking_tick": 2}, {"name": "soup", "position": [2, 3], "_ingredients": [{"name": "onion", "position": [2, 3]}, {"name": "tomato", "position": [2, 3]}], "cooking_tick": 4, "is_cooking": false, "is_ready": true, "is_idle": false, "cook_time": 4, "_cooking_tick": 4}, {"name": "soup", "position": [3, 0], "_ingredients": [{"name": "onion", "position": [3, 0]}, {"name": "tomato", "position": [3, 0]}, {"name": "tomato", "position": [3, 0]}], "cooking_tick": 6, "is_cooking": false, "is_ready": true, "is_idle": false, "cook_time": 6, "_cooking_tick": 6}, {"name": "soup", "position": [2, 0], "_ingredients": [{"name": "tomato", "position": [2, 0]}], "cooking_tick": 2, "is_cooking": false, "is_ready": true, "is_idle": false, "cook_time": 2, "_cooking_tick": 2}], "bonus_orders": [{"ingredients": ["onion", "onion", "tomato"]}], "all_orders": [{"ingredients": ["onion"]}, {"ingredients": ["onion", "onion", "onion"]}, {"ingredients": ["onion", "onion", "tomato"]}], "timestep": 1499} \ No newline at end of file +{"players": [{"position": [3, 2], "orientation": [1, 0], "held_object": {"name": "onion", "position": [3, 2]}}, {"position": [1, 2], "orientation": [-1, 0], "held_object": {"name": "onion", "position": [1, 2]}}], "objects": [{"name": "soup", "position": [1, 0], "_ingredients": [{"name": "tomato", "position": [1, 0]}], "cooking_tick": 2, "is_cooking": false, "is_ready": true, "is_idle": false, "cook_time": 2, "_cooking_tick": 2}, {"name": "soup", "position": [2, 3], "_ingredients": [{"name": "onion", "position": [2, 3]}, {"name": "tomato", "position": [2, 3]}], "cooking_tick": 4, "is_cooking": false, "is_ready": true, "is_idle": false, "cook_time": 4, "_cooking_tick": 4}, {"name": "soup", "position": [3, 0], "_ingredients": [{"name": "onion", "position": [3, 0]}, {"name": "tomato", "position": [3, 0]}, {"name": "tomato", "position": [3, 0]}], "cooking_tick": 6, "is_cooking": false, "is_ready": true, "is_idle": false, "cook_time": 6, "_cooking_tick": 6}, {"name": "soup", "position": [2, 0], "_ingredients": [{"name": "tomato", "position": [2, 0]}], "cooking_tick": 2, "is_cooking": false, "is_ready": true, "is_idle": false, "cook_time": 2, "_cooking_tick": 2}], "bonus_orders": [{"ingredients": ["onion", "onion", "tomato"]}], "all_orders": [{"ingredients": ["onion"]}, {"ingredients": ["onion", "onion", "onion"]}, {"ingredients": ["onion", "onion", "tomato"]}], "timestep": 1407} \ No newline at end of file diff --git a/src/overcooked_ai_py/mdp/overcooked_env.py b/src/overcooked_ai_py/mdp/overcooked_env.py index 862fcbc1..afe2909a 100644 --- a/src/overcooked_ai_py/mdp/overcooked_env.py +++ b/src/overcooked_ai_py/mdp/overcooked_env.py @@ -781,39 +781,35 @@ def proportion_stuck_time(trajectories, agent_idx, stuck_time=3): class Overcooked(gymnasium.Env): """ - Wrapper for the Env class above that is SOMEWHAT compatible with the standard gym API. - Why only somewhat? Because we need to flatten a multi-agent env to be a single-agent env (as gym requires). + Wrapper for the Env class above that is compatible with the Gymnasium API. + Flattens a multi-agent env into a single-agent env (as Gymnasium requires). - NOTE: Observations returned are in a dictionary format with various information that is - necessary to be able to handle the multi-agent nature of the environment. There are probably - better ways to handle this, but we found this to work with minor modifications to OpenAI Baselines. + Observations are returned as a tuple of two numpy arrays (one per agent). + Additional metadata (overcooked_state, other_agent_env_idx) is provided + in the info dict returned by reset() and step(). - NOTE: The index of the main agent (as gym envs are 'single-agent') in the mdp is randomized at each reset + NOTE: The index of the main agent in the mdp is randomized at each reset of the environment, and is kept track of by the self.agent_idx attribute. This means that it is necessary to pass on this information in the output to know for which agent index featurizations should be made for other agents. For example, say one is training A0 paired with A1, and A1 takes a custom state featurization. - Then in the runner.py loop in OpenAI Baselines, we will get the lossless encodings of the state, - and the true Overcooked state. When we encode the true state to feed to A1, we also need to know + When we encode the true state to feed to A1, we also need to know what agent index it has in the environment (as encodings will be index dependent). """ env_name = "Overcooked-v0" - # gym checks for the action space and obs space while initializing the env and throws an error if none exists - # custom_init after __init__ no longer works - # might as well move all the initilization into the actual __init__ def __init__(self, base_env, featurize_fn, baselines_reproducible=False): """ base_env: OvercookedEnv - featurize_fn(mdp, state): fn used to featurize states returned in the 'both_agent_obs' field + featurize_fn(mdp, state): fn used to featurize states returned as observation tuples - Example creating a gym env: + Example: mdp = OvercookedGridworld.from_layout_name("asymmetric_advantages") base_env = OvercookedEnv.from_mdp(mdp, horizon=500) - env = gymnasium.make("Overcooked-v0",base_env = base_env, featurize_fn =base_env.featurize_state_mdp) + env = gymnasium.make("Overcooked-v0", base_env=base_env, featurize_fn=base_env.featurize_state_mdp) """ if baselines_reproducible: # NOTE: @@ -837,9 +833,10 @@ def _setup_observation_space(self): dummy_mdp = self.base_env.mdp dummy_state = dummy_mdp.get_standard_start_state() obs_shape = self.featurize_fn(dummy_state)[0].shape - high = np.ones(obs_shape, dtype=np.float32) * float("inf") - low = np.zeros(obs_shape, dtype=np.float32) - return gymnasium.spaces.Box(low, high, dtype=np.float32) + high = np.full(obs_shape, float("inf"), dtype=np.float64) + low = np.full(obs_shape, float("-inf"), dtype=np.float64) + single_agent_space = gymnasium.spaces.Box(low, high, dtype=np.float64) + return gymnasium.spaces.Tuple((single_agent_space, single_agent_space)) def step(self, action): """ @@ -873,18 +870,16 @@ def step(self, action): both_agents_ob = (ob_p1, ob_p0) env_info["policy_agent_idx"] = self.agent_idx + env_info["overcooked_state"] = next_state + env_info["other_agent_env_idx"] = 1 - self.agent_idx if "episode" in env_info.keys(): env_info["episode"]["policy_agent_idx"] = self.agent_idx - obs = { - "both_agent_obs": both_agents_ob, - "overcooked_state": next_state, - "other_agent_env_idx": 1 - self.agent_idx, - } - return obs, reward, done, env_info + truncated = False # Overcooked episodes end by horizon, not truncation + return both_agents_ob, reward, done, truncated, env_info - def reset(self): + def reset(self, *, seed=None, options=None): """ When training on individual maps, we want to randomize which agent is assigned to which starting location, in order to make sure that the agents are trained to be able to @@ -893,6 +888,7 @@ def reset(self): NOTE: a nicer way to do this would be to just randomize starting positions, and not have to deal with randomizing indices. """ + super().reset(seed=seed, options=options) self.base_env.reset() self.mdp = self.base_env.mdp self.agent_idx = np.random.choice([0, 1]) @@ -902,11 +898,11 @@ def reset(self): both_agents_ob = (ob_p0, ob_p1) else: both_agents_ob = (ob_p1, ob_p0) - return { - "both_agent_obs": both_agents_ob, + info = { "overcooked_state": self.base_env.state, "other_agent_env_idx": 1 - self.agent_idx, } + return both_agents_ob, info def render(self): rewards_dict = {} # dictionary of details you want rendered in the UI diff --git a/src/overcooked_demo/server/Dockerfile b/src/overcooked_demo/server/Dockerfile index 456d45d2..985a42f1 100644 --- a/src/overcooked_demo/server/Dockerfile +++ b/src/overcooked_demo/server/Dockerfile @@ -1,4 +1,4 @@ -FROM python:3.7-buster +FROM python:3.13-bookworm ARG BUILD_ENV ARG OVERCOOKED_BRANCH diff --git a/src/overcooked_demo/server/requirements.txt b/src/overcooked_demo/server/requirements.txt index a9810c76..bc8f9ad8 100644 --- a/src/overcooked_demo/server/requirements.txt +++ b/src/overcooked_demo/server/requirements.txt @@ -1,18 +1,17 @@ -certifi==2020.6.20 -click==8.0 -dnspython==1.16.0 -dill==0.3.2 -Flask==2.1.0 -Flask-SocketIO==4.3.0 -greenlet==0.4.16 -itsdangerous==2.0 -Jinja2==3.1.0 -MarkupSafe==2.0 -monotonic==1.5 -python-engineio==3.13.0 -python-socketio==4.6.0 -six==1.15.0 -Werkzeug==2.0.3 -tensorflow==2.0.3 -requests==2.23.0 -protobuf==3.19 +certifi==2026.2.25 +click==8.3.1 +dnspython==2.8.0 +dill==0.4.1 +Flask==3.1.3 +Flask-SocketIO==5.6.1 +greenlet==3.3.2 +itsdangerous==2.2.0 +Jinja2==3.1.6 +MarkupSafe==3.0.3 +python-engineio==4.13.1 +python-socketio==5.16.1 +six==1.17.0 +Werkzeug==3.1.8 +tensorflow==2.21.0 +requests==2.33.1 +protobuf==7.34.1 diff --git a/testing/agent_test.py b/testing/agent_test.py index b5ac6f95..1e6fca3b 100644 --- a/testing/agent_test.py +++ b/testing/agent_test.py @@ -1,6 +1,5 @@ -import unittest - import numpy as np +import pytest from overcooked_ai_py.agents.agent import ( AgentPair, @@ -38,46 +37,29 @@ large_mdp = OvercookedGridworld.from_layout_name("corridor") -class TestAgentEvaluator(unittest.TestCase): - def setUp(self): +class TestAgentEvaluator: + @pytest.fixture(autouse=True) + def _setup(self): self.agent_eval = AgentEvaluator.from_layout_name( {"layout_name": "cramped_room"}, {"horizon": 100} ) def test_human_model_pair(self): trajs = self.agent_eval.evaluate_human_model_pair() - try: - AgentEvaluator.check_trajectories(trajs, verbose=False) - except AssertionError as e: - self.fail( - "Trajectories were not returned in standard format:\n{}".format( - e - ) - ) + AgentEvaluator.check_trajectories(trajs, verbose=False) def test_rollouts(self): ap = AgentPair(RandomAgent(), RandomAgent()) trajs = self.agent_eval.evaluate_agent_pair(ap, num_games=5) - try: - AgentEvaluator.check_trajectories(trajs, verbose=False) - except AssertionError as e: - self.fail( - "Trajectories were not returned in standard format:\n{}".format( - e - ) - ) + AgentEvaluator.check_trajectories(trajs, verbose=False) def test_mlam_computation(self): - try: - self.agent_eval.env.mlam - except Exception as e: - self.fail( - "Failed to compute MediumLevelActionManager:\n{}".format(e) - ) + self.agent_eval.env.mlam -class TestBasicAgents(unittest.TestCase): - def setUp(self): +class TestBasicAgents: + @pytest.fixture(autouse=True) + def _setup(self): self.mlam_large = MediumLevelActionManager.from_pickle_or_compute( large_mdp, NO_COUNTERS_PARAMS, force_compute=force_compute_large ) @@ -91,10 +73,10 @@ def test_fixed_plan_agents(self): agent_pair, include_final_state=True, display=DISPLAY ) end_state = trajectory[-1][0] - self.assertEqual(time_taken, 10) - self.assertEqual( - env.mdp.get_standard_start_state().player_positions, - end_state.player_positions, + assert time_taken == 10 + assert ( + env.mdp.get_standard_start_state().player_positions + == end_state.player_positions ) def test_two_greedy_human_open_map(self): @@ -132,10 +114,10 @@ def test_sample_agent(self): 0.08333333, ] ) - self.assertTrue(np.allclose(probs, expected_probs)) + assert np.allclose(probs, expected_probs) -class TestAgentEvaluatorStatic(unittest.TestCase): +class TestAgentEvaluatorStatic: layout_name_lst = [ "asymmetric_advantages", "asymmetric_advantages_tomato", @@ -151,37 +133,6 @@ class TestAgentEvaluatorStatic(unittest.TestCase): "you_shall_not_pass", ] - def test_from_mdp(self): - for layout_name in self.layout_name_lst: - orignal_mdp = OvercookedGridworld.from_layout_name(layout_name) - ae = AgentEvaluator.from_mdp( - mdp=orignal_mdp, env_params={"horizon": 400} - ) - ae_mdp = ae.env.mdp - self.assertEqual( - orignal_mdp, - ae_mdp, - "mdp with name " - + layout_name - + " experienced an inconsistency", - ) - - def test_from_mdp_params_layout(self): - for layout_name in self.layout_name_lst: - orignal_mdp = OvercookedGridworld.from_layout_name(layout_name) - ae = AgentEvaluator.from_layout_name( - mdp_params={"layout_name": layout_name}, - env_params={"horizon": 400}, - ) - ae_mdp = ae.env.mdp - self.assertEqual( - orignal_mdp, - ae_mdp, - "mdp with name " - + layout_name - + " experienced an inconsistency", - ) - mdp_gen_params_1 = { "inner_shape": (10, 7), "prop_empty": 0.95, @@ -210,6 +161,39 @@ def test_from_mdp_params_layout(self): outer_shape = (10, 7) + layout_name_short_lst = [ + "cramped_room", + "cramped_room_tomato", + "simple_o", + "simple_tomato", + "simple_o_t", + ] + biased = [0.1, 0.15, 0.2, 0.25, 0.3] + num_reset = 200000 + + def test_from_mdp(self): + for layout_name in self.layout_name_lst: + orignal_mdp = OvercookedGridworld.from_layout_name(layout_name) + ae = AgentEvaluator.from_mdp( + mdp=orignal_mdp, env_params={"horizon": 400} + ) + ae_mdp = ae.env.mdp + assert orignal_mdp == ae_mdp, ( + "mdp with name " + layout_name + " experienced an inconsistency" + ) + + def test_from_mdp_params_layout(self): + for layout_name in self.layout_name_lst: + orignal_mdp = OvercookedGridworld.from_layout_name(layout_name) + ae = AgentEvaluator.from_layout_name( + mdp_params={"layout_name": layout_name}, + env_params={"horizon": 400}, + ) + ae_mdp = ae.env.mdp + assert orignal_mdp == ae_mdp, ( + "mdp with name " + layout_name + " experienced an inconsistency" + ) + def test_from_mdp_params_variable_across(self): for mdp_gen_params in self.mdp_gen_params_lst: ae0 = AgentEvaluator.from_mdp_params_infinite( @@ -222,9 +206,8 @@ def test_from_mdp_params_variable_across(self): env_params={"horizon": 400, "num_mdp": np.inf}, outer_shape=self.outer_shape, ) - self.assertFalse( - ae0.env.mdp == ae1.env.mdp, - "2 randomly generated layouts across 2 evaluators are the same, which is wrong", + assert ae0.env.mdp != ae1.env.mdp, ( + "2 randomly generated layouts across 2 evaluators are the same" ) def test_from_mdp_params_variable_infinite(self): @@ -238,9 +221,8 @@ def test_from_mdp_params_variable_infinite(self): for _ in range(5): ae.env.reset(regen_mdp=True) mdp_1 = ae.env.mdp - self.assertFalse( - mdp_0 == mdp_1, - "with infinite layout generator and regen_mdp=True, the 2 layouts should not be the same", + assert mdp_0 != mdp_1, ( + "with infinite layout generator and regen_mdp=True, layouts should differ" ) def test_from_mdp_params_variable_infinite_no_regen(self): @@ -254,9 +236,8 @@ def test_from_mdp_params_variable_infinite_no_regen(self): for _ in range(5): ae.env.reset(regen_mdp=False) mdp_1 = ae.env.mdp - self.assertTrue( - mdp_0 == mdp_1, - "with infinite layout generator and regen_mdp=False, the 2 layouts should be the same", + assert mdp_0 == mdp_1, ( + "with regen_mdp=False, layouts should be the same" ) def test_from_mdp_params_variable_infinite_specified(self): @@ -270,9 +251,8 @@ def test_from_mdp_params_variable_infinite_specified(self): for _ in range(5): ae.env.reset(regen_mdp=True) mdp_1 = ae.env.mdp - self.assertFalse( - mdp_0 == mdp_1, - "with infinite layout generator and regen_mdp=True, the 2 layouts should not be the same", + assert mdp_0 != mdp_1, ( + "with regen_mdp=True, layouts should differ" ) def test_from_mdp_params_variable_finite(self): @@ -292,24 +272,11 @@ def test_from_mdp_params_variable_finite(self): seen.append(mdp_i.copy()) elif len(seen) == 2: mdp_0, mdp_1 = seen - self.assertTrue( - (mdp_i == mdp_0 or mdp_i == mdp_1), - "more than 2 mdp was created, the function failed to perform", + assert mdp_i == mdp_0 or mdp_i == mdp_1, ( + "more than 2 mdp was created" ) else: - self.assertTrue( - False, "theoretically unreachable statement" - ) - - layout_name_short_lst = [ - "cramped_room", - "cramped_room_tomato", - "simple_o", - "simple_tomato", - "simple_o_t", - ] - biased = [0.1, 0.15, 0.2, 0.25, 0.3] - num_reset = 200000 + assert False, "theoretically unreachable statement" def test_from_mdp_lst_default(self): mdp_lst = [ @@ -320,7 +287,6 @@ def test_from_mdp_lst_default(self): mdp_lst=mdp_lst, env_params={"horizon": 400} ) counts = {} - for _ in range(self.num_reset): ae.env.reset(regen_mdp=True) if ae.env.mdp.layout_name in counts: @@ -329,8 +295,8 @@ def test_from_mdp_lst_default(self): counts[ae.env.mdp.layout_name] = 1 for k, v in counts.items(): - self.assertAlmostEqual( - 0.2, v / self.num_reset, 2, "more than 2 places off for " + k + assert round(v / self.num_reset, 2) == pytest.approx(0.2, abs=0.01), ( + f"more than 2 places off for {k}" ) def test_from_mdp_lst_uniform(self): @@ -344,7 +310,6 @@ def test_from_mdp_lst_uniform(self): sampling_freq=[0.2, 0.2, 0.2, 0.2, 0.2], ) counts = {} - for _ in range(self.num_reset): ae.env.reset(regen_mdp=True) if ae.env.mdp.layout_name in counts: @@ -353,8 +318,8 @@ def test_from_mdp_lst_uniform(self): counts[ae.env.mdp.layout_name] = 1 for k, v in counts.items(): - self.assertAlmostEqual( - 0.2, v / self.num_reset, 2, "more than 2 places off for " + k + assert round(v / self.num_reset, 2) == pytest.approx(0.2, abs=0.01), ( + f"more than 2 places off for {k}" ) def test_from_mdp_lst_biased(self): @@ -368,7 +333,6 @@ def test_from_mdp_lst_biased(self): sampling_freq=self.biased, ) counts = {} - for _ in range(self.num_reset): ae.env.reset(regen_mdp=True) if ae.env.mdp.layout_name in counts: @@ -376,17 +340,11 @@ def test_from_mdp_lst_biased(self): else: counts[ae.env.mdp.layout_name] = 1 - # construct the ground truth gt = { self.layout_name_short_lst[i]: self.biased[i] for i in range(len(self.layout_name_short_lst)) } - for k, v in counts.items(): - self.assertAlmostEqual( - gt[k], v / self.num_reset, 2, "more than 2 places off for " + k + assert round(v / self.num_reset, 2) == pytest.approx(gt[k], abs=0.01), ( + f"more than 2 places off for {k}" ) - - -if __name__ == "__main__": - unittest.main() diff --git a/testing/mdp_gen_schedule_test.py b/testing/mdp_gen_schedule_test.py index e8d04e94..fb2d9c15 100644 --- a/testing/mdp_gen_schedule_test.py +++ b/testing/mdp_gen_schedule_test.py @@ -1,35 +1,12 @@ -import unittest - import numpy as np from overcooked_ai_py.agents.benchmarking import AgentEvaluator -from overcooked_ai_py.mdp.actions import Action, Direction -from overcooked_ai_py.mdp.overcooked_mdp import ( - ObjectState, - OvercookedGridworld, - PlayerState, -) np.random.seed(42) -n, s = Direction.NORTH, Direction.SOUTH -e, w = Direction.EAST, Direction.WEST -stay, interact = Action.STAY, Action.INTERACT -P, Obj = PlayerState, ObjectState - -force_compute_large = False -force_compute = True -DISPLAY = False - -simple_mdp = OvercookedGridworld.from_layout_name("cramped_room") -large_mdp = OvercookedGridworld.from_layout_name("corridor") - def params_schedule_fn_constant_09_01(outside_information): - """ - In this preliminary version, the outside information is ignored - """ - mdp_default_gen_params = { + return { "inner_shape": (7, 5), "prop_empty": 0.9, "prop_feats": 0.1, @@ -37,14 +14,10 @@ def params_schedule_fn_constant_09_01(outside_information): "display": False, "rew_shaping_params": None, } - return mdp_default_gen_params def params_schedule_fn_constant_07_03(outside_info): - """ - In this preliminary version, the outside information is ignored - """ - mdp_default_gen_params = { + return { "inner_shape": (7, 5), "prop_empty": 0.7, "prop_feats": 0.3, @@ -52,14 +25,10 @@ def params_schedule_fn_constant_07_03(outside_info): "display": False, "rew_shaping_params": None, } - return mdp_default_gen_params def params_schedule_fn_constant_05_05(outside_info): - """ - In this preliminary version, the outside information is ignored - """ - mdp_default_gen_params = { + return { "inner_shape": (7, 5), "prop_empty": 0.5, "prop_feats": 0.5, @@ -67,21 +36,16 @@ def params_schedule_fn_constant_05_05(outside_info): "display": False, "rew_shaping_params": None, } - return mdp_default_gen_params def params_schedule_fn_interval(outside_info): - """ - outside_information (dict): - progress (float in [0, 1] interval) a number that indicate progress - """ assert ( outside_info != {} and "progress" in outside_info ), "if this happens during initialization, please add initial_info to env_params to address the issue" progress = outside_info["progress"] prop_empty = 0.9 - 0.4 * progress prop_feats = 0.1 + 0.4 * progress - mdp_params_generated = { + return { "inner_shape": (7, 5), "prop_empty": prop_empty, "prop_feats": prop_feats, @@ -89,7 +53,6 @@ def params_schedule_fn_interval(outside_info): "display": False, "rew_shaping_params": None, } - return mdp_params_generated default_env_params_infinite = {"horizon": 400, "num_mdp": np.inf} @@ -101,7 +64,16 @@ def params_schedule_fn_interval(outside_info): } -class TestParamScheduleFnConstant(unittest.TestCase): +def _avg_empty_grids(ae, num_iters, outside_info_fn=None): + num_empty_grid = [] + for i in range(num_iters): + kwargs = {"outside_info": outside_info_fn(i)} if outside_info_fn else {} + ae.env.reset(**kwargs) + num_empty_grid.append(len(ae.env.mdp.terrain_pos_dict[" "])) + return num_empty_grid + + +class TestParamScheduleFnConstant: def test_constant_schedule_095_01(self): ae = AgentEvaluator.from_mdp_params_infinite( mdp_params=None, @@ -109,16 +81,9 @@ def test_constant_schedule_095_01(self): outer_shape=(7, 5), mdp_params_schedule_fn=params_schedule_fn_constant_09_01, ) - num_empty_grid = [] - for i in range(500): - ae.env.reset() - empty_i = len(ae.env.mdp.terrain_pos_dict[" "]) - num_empty_grid.append(empty_i) + num_empty_grid = _avg_empty_grids(ae, 500) avg_num_empty = sum(num_empty_grid) / len(num_empty_grid) - print("avg number of empty grid:", avg_num_empty) - # the number of empty square should be consistant" - - self.assertTrue(13.9 < avg_num_empty < 14.1) + assert 13.9 < avg_num_empty < 14.1 def test_constant_schedule_07_03(self): ae = AgentEvaluator.from_mdp_params_infinite( @@ -127,15 +92,9 @@ def test_constant_schedule_07_03(self): outer_shape=(7, 5), mdp_params_schedule_fn=params_schedule_fn_constant_07_03, ) - num_empty_grid = [] - for i in range(500): - ae.env.reset() - empty_i = len(ae.env.mdp.terrain_pos_dict[" "]) - num_empty_grid.append(empty_i) + num_empty_grid = _avg_empty_grids(ae, 500) avg_num_empty = sum(num_empty_grid) / len(num_empty_grid) - print("avg number of empty grid:", avg_num_empty) - # the number of empty square should be fairlyconsistant" - self.assertTrue(11.5 < avg_num_empty < 11.8) + assert 11.5 < avg_num_empty < 11.8 def test_constant_schedule_05_05(self): ae = AgentEvaluator.from_mdp_params_infinite( @@ -144,18 +103,12 @@ def test_constant_schedule_05_05(self): outer_shape=(7, 5), mdp_params_schedule_fn=params_schedule_fn_constant_05_05, ) - num_empty_grid = [] - for i in range(500): - ae.env.reset() - empty_i = len(ae.env.mdp.terrain_pos_dict[" "]) - num_empty_grid.append(empty_i) + num_empty_grid = _avg_empty_grids(ae, 500) avg_num_empty = sum(num_empty_grid) / len(num_empty_grid) - print("avg number of empty grid:", avg_num_empty) - # the number of empty square should be fairlyconsistant" - self.assertTrue(10.4 < avg_num_empty < 10.9) + assert 10.3 < avg_num_empty < 11.0 -class TestParamScheduleFnInterval(unittest.TestCase): +class TestParamScheduleFnInterval: def test_interval_schedule(self): ae = AgentEvaluator.from_mdp_params_infinite( mdp_params=None, @@ -163,24 +116,12 @@ def test_interval_schedule(self): outer_shape=(7, 5), mdp_params_schedule_fn=params_schedule_fn_interval, ) - num_empty_grid = [] - for i in range(4000): - ae.env.reset(outside_info={"progress": i / 4000}) - empty_i = len(ae.env.mdp.terrain_pos_dict[" "]) - num_empty_grid.append(empty_i) + num_empty_grid = _avg_empty_grids( + ae, 4000, outside_info_fn=lambda i: {"progress": i / 4000} + ) avg_num_empty_09_01 = sum(num_empty_grid[0:50]) / 50 - self.assertTrue(13.9 < avg_num_empty_09_01 < 14.1) + assert 13.9 < avg_num_empty_09_01 < 14.1 avg_num_empty_07_03 = sum(num_empty_grid[1975:2025]) / 50 - self.assertTrue(11.5 < avg_num_empty_07_03 < 11.8) + assert 11.5 < avg_num_empty_07_03 < 11.8 avg_num_empty_05_05 = sum(num_empty_grid[3950:4000]) / 50 - self.assertTrue(10.4 < avg_num_empty_05_05 < 10.9) - print( - "avg number of empty grids:", - avg_num_empty_09_01, - avg_num_empty_07_03, - avg_num_empty_05_05, - ) - - -if __name__ == "__main__": - unittest.main() + assert 10.3 < avg_num_empty_05_05 < 11.0 diff --git a/testing/overcooked_test.py b/testing/overcooked_test.py index 834cf870..ac86e7a1 100644 --- a/testing/overcooked_test.py +++ b/testing/overcooked_test.py @@ -2,11 +2,12 @@ import json import os import shutil -import unittest +import tempfile from math import factorial import gymnasium import numpy as np +import pytest from overcooked_ai_py.agents.agent import ( AgentGroup, @@ -68,8 +69,9 @@ def comb(n, k): return factorial(n) / (factorial(n - k) * factorial(k)) -class TestRecipe(unittest.TestCase): - def setUp(self): +class TestRecipe: + @pytest.fixture(autouse=True) + def _setup(self): Recipe.configure({}) self.r1 = Recipe([Recipe.ONION, Recipe.ONION, Recipe.ONION]) self.r2 = Recipe([Recipe.ONION, Recipe.ONION, Recipe.ONION]) @@ -85,26 +87,25 @@ def setUp(self): if not os.path.exists(self.pickle_temp_dir): os.makedirs(self.pickle_temp_dir) - def tearDown(self): + yield + Recipe.configure({}) if os.path.exists(self.pickle_temp_dir): shutil.rmtree(self.pickle_temp_dir) def test_eq(self): - self.assertEqual(self.r1, self.r2, "Failed basic equality check") - self.assertNotEqual(self.r1, self.r3, "Failed Basic inequality check") - self.assertNotEqual( - self.r1, self.r6, "Failed inequality check with all one ingredient" - ) - self.assertEqual(self.r3, self.r4, "Failed basic equality check") - self.assertEqual(self.r4, self.r5, "Failed ordered equality check") + assert self.r1 == self.r2, "Failed basic equality check" + assert self.r1 != self.r3, "Failed Basic inequality check" + assert self.r1 != self.r6, "Failed inequality check with all one ingredient" + assert self.r3 == self.r4, "Failed basic equality check" + assert self.r4 == self.r5, "Failed ordered equality check" def test_caching(self): - self.assertIs(self.r1, self.r2) - self.assertIs(self.r3, self.r4) - self.assertIs(self.r4, self.r5) - self.assertFalse(self.r6 is self.r1, "different recipes cached to same value") + assert self.r1 is self.r2 + assert self.r3 is self.r4 + assert self.r4 is self.r5 + assert not (self.r6 is self.r1), "different recipes cached to same value" def test_serialization(self): loaded_recipes = [] @@ -118,63 +119,62 @@ def test_serialization(self): # Ensure loaded recipes equal corresponding original recipe for original, loaded in zip(self.recipes, loaded_recipes): - self.assertEqual(original, loaded) + assert original == loaded def test_value(self): # TODO for recipe in self.recipes: - self.assertEqual(recipe.value, 20) + assert recipe.value == 20 def test_time(self): # TODO for recipe in self.recipes: - self.assertEqual(recipe.time, 20) + assert recipe.time == 20 def test_all_recipes(self): for recipe in self.recipes: - self.assertTrue(recipe in Recipe.ALL_RECIPES) + assert recipe in Recipe.ALL_RECIPES - self.assertEqual( - len(Recipe.ALL_RECIPES), - self._expected_num_recipes( - len(Recipe.ALL_INGREDIENTS), Recipe.MAX_NUM_INGREDIENTS - ), + assert len(Recipe.ALL_RECIPES) == self._expected_num_recipes( + len(Recipe.ALL_INGREDIENTS), Recipe.MAX_NUM_INGREDIENTS ) Recipe.configure({"max_num_ingredients": 4}) - self.assertEqual( - len(Recipe.ALL_RECIPES), - self._expected_num_recipes(len(Recipe.ALL_INGREDIENTS), 4), + assert len(Recipe.ALL_RECIPES) == self._expected_num_recipes( + len(Recipe.ALL_INGREDIENTS), 4 ) def test_invalid_input(self): - self.assertRaises(ValueError, Recipe, [Recipe.ONION, Recipe.TOMATO, "carrot"]) - self.assertRaises(ValueError, Recipe, [Recipe.ONION] * 4) - self.assertRaises(ValueError, Recipe, []) - self.assertRaises(ValueError, Recipe, "invalid argument") + with pytest.raises(ValueError): + Recipe([Recipe.ONION, Recipe.TOMATO, "carrot"]) + with pytest.raises(ValueError): + Recipe([Recipe.ONION] * 4) + with pytest.raises(ValueError): + Recipe([]) + with pytest.raises(ValueError): + Recipe("invalid argument") def test_recipes_generation(self): - self.assertRaises( - AssertionError, - Recipe.generate_random_recipes, - max_size=Recipe.MAX_NUM_INGREDIENTS + 1, - ) - self.assertRaises(AssertionError, Recipe.generate_random_recipes, min_size=0) - self.assertRaises( - AssertionError, - Recipe.generate_random_recipes, - min_size=3, - max_size=2, - ) - self.assertRaises( - AssertionError, - Recipe.generate_random_recipes, - ingredients=["onion", "tomato", "fake_ingredient"], - ) - self.assertRaises(AssertionError, Recipe.generate_random_recipes, n=99999) - self.assertEqual(len(Recipe.generate_random_recipes(n=3)), 3) - self.assertEqual(len(Recipe.generate_random_recipes(n=99, unique=False)), 99) + with pytest.raises(AssertionError): + Recipe.generate_random_recipes( + max_size=Recipe.MAX_NUM_INGREDIENTS + 1, + ) + with pytest.raises(AssertionError): + Recipe.generate_random_recipes(min_size=0) + with pytest.raises(AssertionError): + Recipe.generate_random_recipes( + min_size=3, + max_size=2, + ) + with pytest.raises(AssertionError): + Recipe.generate_random_recipes( + ingredients=["onion", "tomato", "fake_ingredient"], + ) + with pytest.raises(AssertionError): + Recipe.generate_random_recipes(n=99999) + assert len(Recipe.generate_random_recipes(n=3)) == 3 + assert len(Recipe.generate_random_recipes(n=99, unique=False)) == 99 two_sized_recipes = [ Recipe(["onion", "onion"]), @@ -182,8 +182,7 @@ def test_recipes_generation(self): Recipe(["tomato", "tomato"]), ] for _ in range(100): - self.assertCountEqual( - two_sized_recipes, + assert sorted(two_sized_recipes) == sorted( Recipe.generate_random_recipes( n=3, min_size=2, @@ -197,15 +196,13 @@ def test_recipes_generation(self): Recipe(["onion", "onion", "onion"]), ] for _ in range(100): - self.assertCountEqual( - only_onions_recipes, + assert sorted(only_onions_recipes) == sorted( Recipe.generate_random_recipes( n=2, min_size=2, max_size=3, ingredients=["onion"] ), ) - self.assertCountEqual( - only_onions_recipes, + assert sorted(only_onions_recipes) == sorted( set( [ Recipe.generate_random_recipes(n=1, recipes=only_onions_recipes)[0] @@ -218,8 +215,9 @@ def _expected_num_recipes(self, num_ingredients, max_len): return comb(num_ingredients + max_len, num_ingredients) - 1 -class TestSoupState(unittest.TestCase): - def setUp(self): +class TestSoupState: + @pytest.fixture(autouse=True) + def _setup(self): Recipe.configure({}) self.s1 = SoupState.get_soup((0, 0), num_onions=0, num_tomatoes=0) self.s2 = SoupState.get_soup((0, 1), num_onions=2, num_tomatoes=1) @@ -229,120 +227,120 @@ def setUp(self): self.s4 = SoupState.get_soup( (1, 0), num_onions=0, num_tomatoes=2, finished=True ) + yield def test_position(self): new_pos = (2, 0) self.s4.position = new_pos for ingredient in self.s4._ingredients: - self.assertEqual(new_pos, ingredient.position) - self.assertEqual(new_pos, self.s4.position) + assert new_pos == ingredient.position + assert new_pos == self.s4.position def test_is_cooking(self): - self.assertFalse(self.s1.is_cooking) - self.assertFalse(self.s2.is_cooking) - self.assertTrue(self.s3.is_cooking) - self.assertFalse(self.s4.is_cooking) + assert not self.s1.is_cooking + assert not self.s2.is_cooking + assert self.s3.is_cooking + assert not self.s4.is_cooking def test_is_ready(self): - self.assertFalse(self.s1.is_ready) - self.assertFalse(self.s2.is_ready) - self.assertFalse(self.s3.is_ready) - self.assertTrue(self.s4.is_ready) + assert not self.s1.is_ready + assert not self.s2.is_ready + assert not self.s3.is_ready + assert self.s4.is_ready def test_is_idle(self): - self.assertTrue(self.s1.is_idle) - self.assertTrue(self.s2.is_idle) - self.assertFalse(self.s3.is_idle) - self.assertFalse(self.s4.is_idle) + assert self.s1.is_idle + assert self.s2.is_idle + assert not self.s3.is_idle + assert not self.s4.is_idle def test_is_full(self): - self.assertFalse(self.s1.is_full) - self.assertTrue(self.s2.is_full) - self.assertTrue(self.s3.is_full) - self.assertTrue(self.s4.is_full) + assert not self.s1.is_full + assert self.s2.is_full + assert self.s3.is_full + assert self.s4.is_full def test_cooking(self): self.s1.add_ingredient_from_str(Recipe.ONION) self.s1.add_ingredient_from_str(Recipe.TOMATO) - self.assertTrue(self.s1.is_idle) - self.assertFalse(self.s1.is_cooking) - self.assertFalse(self.s1.is_full) + assert self.s1.is_idle + assert not self.s1.is_cooking + assert not self.s1.is_full self.s1.begin_cooking() - self.assertFalse(self.s1.is_idle) - self.assertTrue(self.s1.is_full) - self.assertTrue(self.s1.is_cooking) + assert not self.s1.is_idle + assert self.s1.is_full + assert self.s1.is_cooking for _ in range(self.s1.cook_time): self.s1.cook() - self.assertFalse(self.s1.is_cooking) - self.assertFalse(self.s1.is_idle) - self.assertTrue(self.s1.is_full) - self.assertTrue(self.s1.is_ready) + assert not self.s1.is_cooking + assert not self.s1.is_idle + assert self.s1.is_full + assert self.s1.is_ready def test_attributes(self): - self.assertListEqual(self.s1.ingredients, []) - self.assertListEqual( - self.s2.ingredients, [Recipe.ONION, Recipe.ONION, Recipe.TOMATO] - ) - self.assertListEqual(self.s3.ingredients, [Recipe.ONION]) - self.assertListEqual(self.s4.ingredients, [Recipe.TOMATO, Recipe.TOMATO]) + assert self.s1.ingredients == [] + assert self.s2.ingredients == [Recipe.ONION, Recipe.ONION, Recipe.TOMATO] + assert self.s3.ingredients == [Recipe.ONION] + assert self.s4.ingredients == [Recipe.TOMATO, Recipe.TOMATO] - try: + with pytest.raises(ValueError): self.s1.recipe - self.fail("Expected ValueError to be raised") - except ValueError as e: - pass - except Exception as e: - self.fail("Expected ValueError to be raised, {} raised instead".format(e)) - try: + with pytest.raises(ValueError): self.s2.recipe - self.fail("Expected ValueError to be raised") - except ValueError as e: - pass - except Exception as e: - self.fail("Expected ValueError to be raised, {} raised instead".format(e)) - self.assertEqual(self.s3.recipe, Recipe([Recipe.ONION])) - self.assertEqual(self.s4.recipe, Recipe([Recipe.TOMATO, Recipe.TOMATO])) + + assert self.s3.recipe == Recipe([Recipe.ONION]) + assert self.s4.recipe == Recipe([Recipe.TOMATO, Recipe.TOMATO]) def test_invalid_ops(self): # Cannot cook an empty soup - self.assertRaises(ValueError, self.s1.begin_cooking) + with pytest.raises(ValueError): + self.s1.begin_cooking() # Must call 'begin_cooking' before cooking a soup - self.assertRaises(ValueError, self.s2.cook) + with pytest.raises(ValueError): + self.s2.cook() # Cannot cook a done soup - self.assertRaises(ValueError, self.s4.cook) + with pytest.raises(ValueError): + self.s4.cook() # Cannot begin cooking a soup that is already cooking - self.assertRaises(ValueError, self.s3.begin_cooking) + with pytest.raises(ValueError): + self.s3.begin_cooking() # Cannot begin cooking a soup that is already done - self.assertRaises(ValueError, self.s4.begin_cooking) + with pytest.raises(ValueError): + self.s4.begin_cooking() # Cannot add ingredients to a soup that is cooking - self.assertRaises(ValueError, self.s3.add_ingredient_from_str, Recipe.ONION) + with pytest.raises(ValueError): + self.s3.add_ingredient_from_str(Recipe.ONION) # Cannot add ingredients to a soup that is ready - self.assertRaises(ValueError, self.s4.add_ingredient_from_str, Recipe.ONION) + with pytest.raises(ValueError): + self.s4.add_ingredient_from_str(Recipe.ONION) # Cannot remove an ingredient from a soup that is ready - self.assertRaises(ValueError, self.s4.pop_ingredient) + with pytest.raises(ValueError): + self.s4.pop_ingredient() # Cannot remove an ingredient from a soup that is cooking - self.assertRaises(ValueError, self.s3.pop_ingredient) + with pytest.raises(ValueError): + self.s3.pop_ingredient() # Cannot remove an ingredient from a soup that is empty - self.assertRaises(ValueError, self.s1.pop_ingredient) + with pytest.raises(ValueError): + self.s1.pop_ingredient() -class TestDirection(unittest.TestCase): +class TestDirection: def test_direction_number_conversion(self): all_directions = Direction.ALL_DIRECTIONS all_numbers = [] @@ -350,48 +348,50 @@ def test_direction_number_conversion(self): for direction in Direction.ALL_DIRECTIONS: number = Direction.DIRECTION_TO_INDEX[direction] direction_again = Direction.INDEX_TO_DIRECTION[number] - self.assertEqual(direction, direction_again) + assert direction == direction_again all_numbers.append(number) # Check that all directions are distinct num_directions = len(all_directions) - self.assertEqual(len(set(all_directions)), num_directions) + assert len(set(all_directions)) == num_directions # Check that the numbers are 0, 1, ... num_directions - 1 - self.assertEqual(set(all_numbers), set(range(num_directions))) + assert set(all_numbers) == set(range(num_directions)) -class TestGridworld(unittest.TestCase): +class TestGridworld: # TODO: write more smaller targeted tests to be loaded from jsons verbose = False - def setUp(self): + @pytest.fixture(autouse=True) + def _setup(self): self.base_mdp = OvercookedGridworld.from_layout_name("mdp_test") + yield def test_constructor_invalid_inputs(self): # Height and width must be at least 3. - with self.assertRaises(AssertionError): + with pytest.raises(AssertionError): mdp = OvercookedGridworld.from_grid(["X", "X", "X"]) - with self.assertRaises(AssertionError): + with pytest.raises(AssertionError): mdp = OvercookedGridworld.from_grid([["X", "X", "X"]]) - with self.assertRaises(AssertionError): + with pytest.raises(AssertionError): # Borders must be present. mdp = OvercookedGridworld.from_grid(["XOSX", "P D", " 21 "]) - with self.assertRaises(AssertionError): + with pytest.raises(AssertionError): # The grid can't be ragged. mdp = OvercookedGridworld.from_grid(["XXPXX", "O 2XX", "X1 3 X", "XDXSXX"]) - with self.assertRaises(AssertionError): + with pytest.raises(AssertionError): # The agents must be numbered 1 and 2. mdp = OvercookedGridworld.from_grid(["XXPXX", "O 3O", "X1 X", "XDXSX"]) - with self.assertRaises(AssertionError): + with pytest.raises(AssertionError): # The agents must be numbered 1 and 2. mdp = OvercookedGridworld.from_grid(["XXPXX", "O 1O", "X1 X", "XDXSX"]) - with self.assertRaises(AssertionError): + with pytest.raises(AssertionError): # B is not a valid element. mdp = OvercookedGridworld.from_grid(["XBPXX", "O 2O", "X1 X", "XDXSX"]) @@ -407,11 +407,8 @@ def test_start_positions(self): expected_start_state = OvercookedState.from_dict( load_from_json(expected_state_path) ) - self.assertEqual( - actual_start_state, - expected_start_state, - "\n" + str(actual_start_state) + "\n" + str(expected_start_state), - ) + assert actual_start_state == expected_start_state, \ + "\n" + str(actual_start_state) + "\n" + str(expected_start_state) def test_file_constructor(self): mdp = OvercookedGridworld.from_layout_name("corridor") @@ -424,11 +421,8 @@ def test_file_constructor(self): all_orders=[{"ingredients": ["onion", "onion", "onion"]}], ) actual_start_state = mdp.get_standard_start_state() - self.assertEqual( - actual_start_state, - expected_start_state, - "\n" + str(actual_start_state) + "\n" + str(expected_start_state), - ) + assert actual_start_state == expected_start_state, \ + "\n" + str(actual_start_state) + "\n" + str(expected_start_state) def test_actions(self): bad_state = OvercookedState( @@ -438,13 +432,11 @@ def test_actions(self): ], {}, ) - with self.assertRaises(AssertionError): + with pytest.raises(AssertionError): self.base_mdp.get_actions(bad_state) - self.assertEqual( - self.base_mdp.get_actions(self.base_mdp.get_standard_start_state()), - [Action.ALL_ACTIONS, Action.ALL_ACTIONS], - ) + assert self.base_mdp.get_actions(self.base_mdp.get_standard_start_state()) == \ + [Action.ALL_ACTIONS, Action.ALL_ACTIONS] def test_from_dict(self): state_dict = { @@ -468,7 +460,7 @@ def test_from_dict(self): def test_transitions_and_environment(self): bad_state = OvercookedState([P((0, 0), s), P((3, 1), s)], {}) - with self.assertRaises(AssertionError): + with pytest.raises(AssertionError): self.base_mdp.get_state_transition(bad_state, stay) env = OvercookedEnv.from_mdp(self.base_mdp, info_level=0) @@ -478,11 +470,8 @@ def check_transition(action, expected_path, recompute=False): state = env.state pred_state, _ = self.base_mdp.get_state_transition(state, action) new_state, sparse_reward, _, _ = env.step(action) - self.assertEqual( - pred_state, - new_state, - "\n" + str(pred_state) + "\n" + str(new_state), - ) + assert pred_state == new_state, \ + "\n" + str(pred_state) + "\n" + str(new_state) # Recompute expected values if desired if recompute: @@ -498,11 +487,9 @@ def check_transition(action, expected_path, recompute=False): expected_reward = expected["reward"] # Make sure everything lines up (note __eq__ is transitive) - self.assertTrue( - pred_state.time_independent_equal(expected_state), - "\n" + str(pred_state) + "\n" + str(expected_state), - ) - self.assertEqual(sparse_reward, expected_reward) + assert pred_state.time_independent_equal(expected_state), \ + "\n" + str(pred_state) + "\n" + str(expected_state) + assert sparse_reward == expected_reward expected_path = os.path.join( TESTING_DATA_DIR, @@ -525,7 +512,7 @@ def test_mdp_dynamics(self): ) def test_mdp_old_cook_dynamics(self): - with self.assertRaises(AssertionError): + with pytest.raises(AssertionError): # shouldn't be able to create a game with recipes of less than 3 ingredients OvercookedGridworld.from_layout_name( layout_name="mdp_test", old_dynamics=True @@ -540,11 +527,11 @@ def test_mdp_old_cook_dynamics(self): # test interacting with a 1-ingredient soup starts cooking in the new dynamics new_state_n, _ = new_mdp.get_state_transition(new_mdp.start_state, [interact]) soup_new = new_state_n.get_object((2, 0)) - self.assertTrue(soup_new.is_cooking) + assert soup_new.is_cooking # this should have no effects new_state_o, _ = old_mdp.get_state_transition(old_mdp.start_state, [interact]) soup_old = new_state_o.get_object((2, 0)) - self.assertFalse(soup_old.is_cooking) + assert not soup_old.is_cooking def test_mdp_old_put_dynamics(self): old_mdp = OvercookedGridworld.from_layout_name( @@ -556,47 +543,43 @@ def test_mdp_old_put_dynamics(self): # putting in the third ingredient will not start cooking in the new dynamics new_state_n, _ = new_mdp.get_state_transition(new_mdp.start_state, [interact]) soup_new = new_state_n.get_object((2, 0)) - self.assertFalse(soup_new.is_cooking) + assert not soup_new.is_cooking # cooking should start automatically in the old dynamics new_state_o, _ = old_mdp.get_state_transition(old_mdp.start_state, [interact]) soup_old = new_state_o.get_object((2, 0)) - self.assertTrue(soup_old.is_cooking) + assert soup_old.is_cooking def test_mdp_serialization(self): - # Where to store serialized states -- will be overwritten each timestep - dummy_path = os.path.join( - TESTING_DATA_DIR, "test_mdp_serialization", "dummy.json" - ) + # Use a temp file to avoid file contention when tests run concurrently + with tempfile.TemporaryDirectory() as tmpdir: + dummy_path = os.path.join(tmpdir, "dummy.json") + + # Get starting seed and random agent pair + seed = 47 + random_pair = AgentPair( + RandomAgent(all_actions=True), RandomAgent(all_actions=True) + ) - # Get starting seed and random agent pair - seed = 47 - random_pair = AgentPair( - RandomAgent(all_actions=True), RandomAgent(all_actions=True) - ) + # Run rollouts with different seeds until sparse reward is achieved + sparse_reward = 0 + while sparse_reward <= 0: + np.random.seed(seed) + state = self.base_mdp.get_standard_start_state() + for _ in range(1500): + # Ensure serialization and deserializations are inverses + reconstructed_state = OvercookedState.from_dict( + load_from_json(save_as_json(state.to_dict(), dummy_path)) + ) + assert state == reconstructed_state, \ + "\nState: \t\t\t{}\nReconstructed State: \t{}".format( + state, reconstructed_state + ) - # Run rollouts with different seeds until sparse reward is achieved - sparse_reward = 0 - while sparse_reward <= 0: - np.random.seed(seed) - state = self.base_mdp.get_standard_start_state() - for _ in range(1500): - # Ensure serialization and deserializations are inverses - reconstructed_state = OvercookedState.from_dict( - load_from_json(save_as_json(state.to_dict(), dummy_path)) - ) - self.assertEqual( - state, - reconstructed_state, - "\nState: \t\t\t{}\nReconstructed State: \t{}".format( - state, reconstructed_state - ), - ) - - # Advance state - joint_action, _ = zip(*random_pair.joint_action(state)) - state, infos = self.base_mdp.get_state_transition(state, joint_action) - sparse_reward += sum(infos["sparse_reward_by_agent"]) - seed += 1 + # Advance state + joint_action, _ = zip(*random_pair.joint_action(state)) + state, infos = self.base_mdp.get_state_transition(state, joint_action) + sparse_reward += sum(infos["sparse_reward_by_agent"]) + seed += 1 def test_four_player_mdp(self): try: @@ -633,8 +616,8 @@ def test_potential_function(self): print("potential: ", self.base_mdp.potential_function(state, mp)) val2 = self.base_mdp.potential_function(state, mp) - self.assertLess(val0, val1, "Picking up onion should increase potential") - self.assertLess(val1, val2, "Picking up tomato should increase potential") + assert val0 < val1, "Picking up onion should increase potential" + assert val1 < val2, "Picking up tomato should increase potential" # Pot tomato if self.verbose: @@ -663,8 +646,8 @@ def test_potential_function(self): print("potential: ", self.base_mdp.potential_function(state, mp)) val4 = self.base_mdp.potential_function(state, mp) - self.assertLess(val2, val3, "Potting tomato should increase potential") - self.assertLess(val3, val4, "Potting onion should increase potential") + assert val2 < val3, "Potting tomato should increase potential" + assert val3 < val4, "Potting onion should increase potential" ## Repeat on second pot ## @@ -690,8 +673,8 @@ def test_potential_function(self): print("potential: ", self.base_mdp.potential_function(state, mp)) val6 = self.base_mdp.potential_function(state, mp) - self.assertLess(val4, val5, "Picking up onion should increase potential") - self.assertLess(val5, val6, "Picking up tomato should increase potential") + assert val4 < val5, "Picking up onion should increase potential" + assert val5 < val6, "Picking up tomato should increase potential" # Pot onion if self.verbose: @@ -721,8 +704,8 @@ def test_potential_function(self): print("potential: ", self.base_mdp.potential_function(state, mp)) val8 = self.base_mdp.potential_function(state, mp) - self.assertLess(val6, val7, "Potting onion should increase potential") - self.assertLess(val7, val8, "Potting tomato should increase potential") + assert val6 < val7, "Potting onion should increase potential" + assert val7 < val8, "Potting tomato should increase potential" ## Useless pickups ## @@ -748,12 +731,8 @@ def test_potential_function(self): print("potential: ", self.base_mdp.potential_function(state, mp)) val10 = self.base_mdp.potential_function(state, mp) - self.assertLessEqual( - val9, val8, "Extraneous pickup should not increase potential" - ) - self.assertLessEqual( - val10, val8, "Extraneous pickup should not increase potential" - ) + assert val9 <= val8, "Extraneous pickup should not increase potential" + assert val10 <= val8, "Extraneous pickup should not increase potential" ## Catastrophic soup failure ## @@ -768,7 +747,7 @@ def test_potential_function(self): print("potential: ", self.base_mdp.potential_function(state, mp)) val11 = self.base_mdp.potential_function(state, mp) - self.assertLess(val11, val10, "Catastrophic potting should decrease potential") + assert val11 < val10, "Catastrophic potting should decrease potential" ## Bonus soup creation @@ -810,9 +789,9 @@ def test_potential_function(self): print("potential: ", self.base_mdp.potential_function(state, mp)) val14 = self.base_mdp.potential_function(state, mp) - self.assertLess(val11, val12, "Useful onion pickup should increase potential") - self.assertLess(val12, val13, "Potting useful onion should increase potential") - self.assertLess(val13, val14, "Cooking optimal soup should increase potential") + assert val11 < val12, "Useful onion pickup should increase potential" + assert val12 < val13, "Potting useful onion should increase potential" + assert val13 < val14, "Cooking optimal soup should increase potential" ## Soup pickup ## @@ -849,13 +828,9 @@ def test_potential_function(self): print("potential: ", self.base_mdp.potential_function(state, mp)) val17 = self.base_mdp.potential_function(state, mp) - self.assertLess(val14, val15, "Useful dish pickups should increase potential") - self.assertLess( - val15, - val16, - "Moving towards soup with dish should increase potential", - ) - self.assertLess(val16, val17, "Picking up soup should increase potential") + assert val14 < val15, "Useful dish pickups should increase potential" + assert val15 < val16, "Moving towards soup with dish should increase potential" + assert val16 < val17, "Picking up soup should increase potential" ## Removing failed soup from pot @@ -901,18 +876,11 @@ def test_potential_function(self): print("potential: ", self.base_mdp.potential_function(state, mp)) val21 = self.base_mdp.potential_function(state, mp) - self.assertLess( - val17, - val18, - "Moving towards failed soup should increase potential", - ) - self.assertLess(val18, val19, "Cooking failed soup should increase potential") - self.assertLess(val19, val20, "Dish pickup for failed soup is still useful") - self.assertLess( - val20, - val21, - "Moving towars pertinant pot with dish should increase potential", - ) + assert val17 < val18, "Moving towards failed soup should increase potential" + assert val18 < val19, "Cooking failed soup should increase potential" + assert val19 < val20, "Dish pickup for failed soup is still useful" + assert val20 < val21, \ + "Moving towars pertinant pot with dish should increase potential" ## Deliver failed soup ## @@ -947,21 +915,11 @@ def test_potential_function(self): print("potential: ", self.base_mdp.potential_function(state, mp)) val24 = self.base_mdp.potential_function(state, mp) - self.assertLess( - val21, val22, "Picking up failed soup should increase potential" - ) - self.assertAlmostEqual( - val23, - val22, - delta=0.2, - msg="Moving to serve failed soup doesn't change potential much", - ) - self.assertAlmostEqual( - val23, - val24, - delta=0.2, - msg="Moving away from serving area with failed soup doesn't change much", - ) + assert val21 < val22, "Picking up failed soup should increase potential" + assert val23 == pytest.approx(val22, abs=0.2), \ + "Moving to serve failed soup doesn't change potential much" + assert val23 == pytest.approx(val24, abs=0.2), \ + "Moving away from serving area with failed soup doesn't change much" ## Deliver successful soup ## @@ -986,16 +944,10 @@ def test_potential_function(self): print(self.base_mdp.state_string(state)) print("potential: ", self.base_mdp.potential_function(state, mp)) - self.assertLess( - val24, - val25, - "Moving towards serving area with valid soup increases potential", - ) - self.assertEqual( - sum(rewards["sparse_reward_by_agent"]), - 50, - "Soup was not properly devivered, probably an error with MDP logic", - ) + assert val24 < val25, \ + "Moving towards serving area with valid soup increases potential" + assert sum(rewards["sparse_reward_by_agent"]) == 50, \ + "Soup was not properly devivered, probably an error with MDP logic" def random_joint_action(): @@ -1004,8 +956,9 @@ def random_joint_action(): return (Action.INDEX_TO_ACTION[a_idx0], Action.INDEX_TO_ACTION[a_idx1]) -class TestFeaturizations(unittest.TestCase): - def setUp(self): +class TestFeaturizations: + @pytest.fixture(autouse=True) + def _setup(self): self.base_mdp = OvercookedGridworld.from_layout_name("cramped_room") self.mlam = MediumLevelActionManager.from_pickle_or_compute( self.base_mdp, NO_COUNTERS_PARAMS, force_compute=True @@ -1017,17 +970,15 @@ def setUp(self): GreedyHumanModel(self.mlam), GreedyHumanModel(self.mlam) ) np.random.seed(0) + yield def test_lossless_state_featurization_shape(self): s = self.base_mdp.get_standard_start_state() obs = self.base_mdp.lossless_state_encoding(s)[0] - self.assertTrue( - np.array_equal( - obs.shape, self.base_mdp.get_lossless_state_encoding_shape() - ), - "{} vs {}".format( - obs.shape, self.base_mdp.get_lossless_state_encoding_shape() - ), + assert np.array_equal( + obs.shape, self.base_mdp.get_lossless_state_encoding_shape() + ), "{} vs {}".format( + obs.shape, self.base_mdp.get_lossless_state_encoding_shape() ) def test_state_featurization_shape(self): @@ -1038,14 +989,10 @@ def test_state_featurization_shape(self): s, self.mlam, num_pots=num_pots ) expected_shape = self.base_mdp.get_featurize_state_shape(num_pots=num_pots) - self.assertTrue( - np.array_equal(obs_0.shape, expected_shape), - "{} vs {}".format(obs_0.shape, expected_shape), - ) - self.assertTrue( - np.array_equal(obs_1.shape, expected_shape), - "{} vs {}".format(obs_1.shape, expected_shape), - ) + assert np.array_equal(obs_0.shape, expected_shape), \ + "{} vs {}".format(obs_0.shape, expected_shape) + assert np.array_equal(obs_1.shape, expected_shape), \ + "{} vs {}".format(obs_1.shape, expected_shape) def test_lossless_state_featurization(self): trajs = self.env.get_rollouts( @@ -1064,7 +1011,7 @@ def test_lossless_state_featurization(self): # featurizations by uncommenting the following line: # save_pickle(featurized_observations, pickle_path) expected_featurization = load_pickle(pickle_path) - self.assertTrue(np.array_equal(expected_featurization, featurized_observations)) + assert np.array_equal(expected_featurization, featurized_observations) def test_state_featurization(self): trajs = self.env.get_rollouts( @@ -1088,9 +1035,7 @@ def test_state_featurization(self): # featurizations by uncommenting the following line: # save_pickle(featurized_observations, pickle_path) expected_featurization = load_pickle(pickle_path) - self.assertTrue( - np.array_equal(expected_featurization, featurized_observations) - ) + assert np.array_equal(expected_featurization, featurized_observations) def test_state_featurization_symmetry(self): trajs = self.env.get_rollouts( @@ -1106,8 +1051,8 @@ def test_state_featurization_symmetry(self): ) state = state.reverse_players() - self.assertTrue(np.array_equal(p0_obs, p1_obs_swapped)) - self.assertTrue(np.array_equal(p1_obs, p0_obs_swapped)) + assert np.array_equal(p0_obs, p1_obs_swapped) + assert np.array_equal(p1_obs, p0_obs_swapped) def test_lossless_state_featurization_symmetry(self): trajs = self.env.get_rollouts( @@ -1124,14 +1069,25 @@ def test_lossless_state_featurization_symmetry(self): ) = self.base_mdp.lossless_state_encoding(state) state = state.reverse_players() - self.assertTrue(np.array_equal(p0_obs, p1_obs_swapped)) - self.assertTrue(np.array_equal(p1_obs, p0_obs_swapped)) + assert np.array_equal(p0_obs, p1_obs_swapped) + assert np.array_equal(p1_obs, p0_obs_swapped) + + +def _assert_files_equal(file_1, file_2): + with open(file_1, "r", encoding="utf-8") as f: + lines_1 = f.readlines() + with open(file_2, "r", encoding="utf-8") as f: + lines_2 = f.readlines() + for line_1, line_2 in zip(lines_1, lines_2): + assert line_1 == line_2 -class TestOvercookedEnvironment(unittest.TestCase): + +class TestOvercookedEnvironment: dummy_dir = "overcooked_test_temp" - def setUp(self): + @pytest.fixture(autouse=True) + def _setup(self): if not os.path.exists(self.dummy_dir): os.makedirs(self.dummy_dir) self.base_mdp = OvercookedGridworld.from_layout_name("cramped_room") @@ -1143,25 +1099,14 @@ def setUp(self): ) np.random.seed(0) - def tearDown(self): - shutil.rmtree(self.dummy_dir) - - def _assert_files_equal(self, file_1, file_2): - with open(file_1, "r", encoding="utf-8") as f: - lines_1 = f.readlines() - with open(file_2, "r", encoding="utf-8") as f: - lines_2 = f.readlines() + yield - for line_1, line_2 in zip(lines_1, lines_2): - self.assertEqual(line_1, line_2) + shutil.rmtree(self.dummy_dir) def test_constructor(self): - try: - OvercookedEnv.from_mdp(self.base_mdp, horizon=10, info_level=0) - except Exception as e: - self.fail("Failed to instantiate OvercookedEnv:\n{}".format(e)) + OvercookedEnv.from_mdp(self.base_mdp, horizon=10, info_level=0) - with self.assertRaises(TypeError): + with pytest.raises(TypeError): OvercookedEnv.from_mdp(self.base_mdp, **{"invalid_env_param": None}) def test_step_fn(self): @@ -1176,14 +1121,10 @@ def test_execute_plan(self): def test_run_agents(self): start_state = self.env.state self.env.run_agents(self.rnd_agent_pair) - self.assertNotEqual(self.env.state, start_state) + assert self.env.state != start_state def test_rollouts(self): - try: - self.env.get_rollouts(self.rnd_agent_pair, 3, info=False) - except Exception as e: - print(e.with_traceback()) - self.fail("Failed to get rollouts from environment:\n{}".format(e)) + self.env.get_rollouts(self.rnd_agent_pair, 3, info=False) def test_one_player_env(self): mdp = OvercookedGridworld.from_layout_name("cramped_room_single") @@ -1191,7 +1132,7 @@ def test_one_player_env(self): a0 = FixedPlanAgent([stay, w, w, e, e, n, e, interact, w, n, interact]) ag = AgentGroup(a0) env.run_agents(ag, display=False) - self.assertEqual(env.state.players_pos_and_or, (((2, 1), (0, -1)),)) + assert env.state.players_pos_and_or == (((2, 1), (0, -1)),) def test_four_player_env_fixed(self): mdp = OvercookedGridworld.from_layout_name("multiplayer_schelling") @@ -1223,14 +1164,11 @@ def test_four_player_env_fixed(self): a3 = FixedPlanAgent([e, interact, n, n, w, w, w, n, interact, e, s]) ag = AgentGroup(a0, a1, a2, a3) env.run_agents(ag, display=False) - self.assertEqual( - env.state.players_pos_and_or, - ( - ((1, 1), (-1, 0)), - ((3, 1), (0, -1)), - ((2, 1), (-1, 0)), - ((4, 2), (0, 1)), - ), + assert env.state.players_pos_and_or == ( + ((1, 1), (-1, 0)), + ((3, 1), (0, -1)), + ((2, 1), (-1, 0)), + ((4, 2), (0, 1)), ) def test_display(self): @@ -1252,7 +1190,7 @@ def test_display(self): # If display intentionally updated, uncomment the line below to update expected values shutil.copy(actual_display_file, expected_display_file) - self._assert_files_equal(expected_display_file, actual_display_file) + _assert_files_equal(expected_display_file, actual_display_file) def test_display_phi(self): mdp0 = OvercookedGridworld.from_layout_name("cramped_room") @@ -1274,7 +1212,7 @@ def test_display_phi(self): # If display intentionally updated, uncomment the line below to update expected values shutil.copy(actual_display_file, expected_display_file) - self._assert_files_equal(expected_display_file, actual_display_file) + _assert_files_equal(expected_display_file, actual_display_file) def test_multiple_mdp_env(self): mdp0 = OvercookedGridworld.from_layout_name("cramped_room") @@ -1295,7 +1233,7 @@ def test_starting_position_randomization(self): for _ in range(3): env.reset() curr_terrain = env.state.players_pos_and_or - self.assertFalse(np.array_equal(start_state, curr_terrain)) + assert not np.array_equal(start_state, curr_terrain) def test_starting_obj_randomization(self): self.base_mdp = OvercookedGridworld.from_layout_name("cramped_room") @@ -1307,10 +1245,10 @@ def test_starting_obj_randomization(self): for _ in range(3): env.reset() curr_terrain = env.state.all_objects_list - self.assertFalse(np.array_equal(start_state, curr_terrain)) + assert not np.array_equal(start_state, curr_terrain) def test_failing_rnd_layout(self): - with self.assertRaises(TypeError): + with pytest.raises(TypeError): mdp_gen_params = {"None": None} mdp_fn = LayoutGenerator.mdp_gen_fn_from_dict(**mdp_gen_params) OvercookedEnv(mdp_fn, **DEFAULT_ENV_PARAMS) @@ -1334,7 +1272,7 @@ def test_random_layout(self): for _ in range(3): env.reset() curr_terrain = env.mdp.terrain_mtx - self.assertFalse(np.array_equal(start_terrain, curr_terrain)) + assert not np.array_equal(start_terrain, curr_terrain) mdp_gen_params = {"layout_name": "cramped_room"} mdp_fn = LayoutGenerator.mdp_gen_fn_from_dict(mdp_gen_params) @@ -1348,7 +1286,7 @@ def test_random_layout(self): all_same_layout = all( [np.array_equal(env.mdp.terrain_mtx, terrain) for terrain in layouts_seen] ) - self.assertTrue(all_same_layout) + assert all_same_layout mdp_gen_params = {"layout_name": "asymmetric_advantages"} mdp_fn = LayoutGenerator.mdp_gen_fn_from_dict(mdp_gen_params) @@ -1360,7 +1298,7 @@ def test_random_layout(self): all_same_layout = all( [np.array_equal(env.mdp.terrain_mtx, terrain) for terrain in layouts_seen] ) - self.assertFalse(all_same_layout) + assert not all_same_layout def test_random_layout_feature_types(self): mandatory_features = {POT, DISH_DISPENSER, SERVING_LOC} @@ -1390,15 +1328,11 @@ def test_random_layout_feature_types(self): env.reset() curr_terrain = env.mdp.terrain_mtx terrain_features = set.union(*(set(line) for line in curr_terrain)) - self.assertTrue( - all(elem in terrain_features for elem in used_features) - ) # all used_features are actually used + assert all(elem in terrain_features for elem in used_features) # all used_features are actually used if left_out_optional_features: - self.assertFalse( - any( - elem in terrain_features - for elem in left_out_optional_features - ) + assert not any( + elem in terrain_features + for elem in left_out_optional_features ) # all left_out optional_features are not used def test_random_layout_generated_recipes(self): @@ -1427,8 +1361,8 @@ def test_random_layout_generated_recipes(self): env = OvercookedEnv(mdp_fn, **DEFAULT_ENV_PARAMS) for _ in range(10): env.reset() - self.assertCountEqual(env.mdp.start_all_orders, only_onions_dict_recipes) - self.assertEqual(len(env.mdp.start_bonus_orders), 0) + assert sorted(env.mdp.start_all_orders, key=lambda d: json.dumps(d, sort_keys=True)) == sorted(only_onions_dict_recipes, key=lambda d: json.dumps(d, sort_keys=True)) + assert len(env.mdp.start_bonus_orders) == 0 # checking if bonus_orders is subset of all_orders even if not specified @@ -1451,9 +1385,9 @@ def test_random_layout_generated_recipes(self): env = OvercookedEnv(mdp_fn, **DEFAULT_ENV_PARAMS) for _ in range(10): env.reset() - self.assertCountEqual(env.mdp.start_all_orders, only_onions_dict_recipes) - self.assertEqual(len(env.mdp.start_bonus_orders), 1) - self.assertTrue(env.mdp.start_bonus_orders[0] in only_onions_dict_recipes) + assert sorted(env.mdp.start_all_orders, key=lambda d: json.dumps(d, sort_keys=True)) == sorted(only_onions_dict_recipes, key=lambda d: json.dumps(d, sort_keys=True)) + assert len(env.mdp.start_bonus_orders) == 1 + assert env.mdp.start_bonus_orders[0] in only_onions_dict_recipes # checking if after reset there are new recipes generated mdp_gen_params = { @@ -1480,15 +1414,17 @@ def test_random_layout_generated_recipes(self): generated_recipes_strings |= { json.dumps(o, sort_keys=True) for o in env.mdp.start_all_orders } - self.assertTrue(len(generated_recipes_strings) > 3) + assert len(generated_recipes_strings) > 3 -class TestGymEnvironment(unittest.TestCase): - def setUp(self): +class TestGymEnvironment: + @pytest.fixture(autouse=True) + def _setup(self): self.base_mdp = OvercookedGridworld.from_layout_name("cramped_room") self.env = OvercookedEnv.from_mdp(self.base_mdp, **DEFAULT_ENV_PARAMS) self.rnd_agent_pair = AgentPair(FixedPlanAgent([]), FixedPlanAgent([])) np.random.seed(0) + yield def test_creation(self): env = gymnasium.make( @@ -1497,17 +1433,19 @@ def test_creation(self): featurize_fn=self.env.featurize_state_mdp, ) # verify that the action_space * obs_space are initialized correctly - self.assertEqual(env.action_space, gymnasium.spaces.Discrete(6)) - self.assertEqual( - env.observation_space.shape, - self.base_mdp.get_featurize_state_shape(), - ) + assert env.action_space == gymnasium.spaces.Discrete(6) + expected_shape = self.base_mdp.get_featurize_state_shape() + assert isinstance(env.observation_space, gymnasium.spaces.Tuple) + assert len(env.observation_space.spaces) == 2 + assert env.observation_space.spaces[0].shape == expected_shape + assert env.observation_space.spaces[1].shape == expected_shape # TODO: write more tests here -class TestTrajectories(unittest.TestCase): - def setUp(self): +class TestTrajectories: + @pytest.fixture(autouse=True) + def _setup(self): self.base_mdp = OvercookedGridworld.from_layout_name("cramped_room") self.mlam = MediumLevelActionManager.from_pickle_or_compute( self.base_mdp, NO_COUNTERS_PARAMS, force_compute=True @@ -1519,6 +1457,7 @@ def setUp(self): GreedyHumanModel(self.mlam), GreedyHumanModel(self.mlam) ) np.random.seed(0) + yield def test_append(self): traj_one = self.env.get_rollouts( @@ -1531,22 +1470,18 @@ def test_append(self): combined = append_trajectories(traj_one, traj_two) # Ensure proper keys - self.assertEqual(set(combined.keys()), DEFAULT_TRAJ_KEYS) + assert set(combined.keys()) == DEFAULT_TRAJ_KEYS # Ensure proper shapes for key in TIMESTEP_TRAJ_KEYS: shape_one = traj_one[key].shape shape_two = traj_two[key].shape shape_combined = combined[key].shape - self.assertEqual(shape_combined[0], shape_one[0] + shape_two[0]) - self.assertEqual(shape_combined[1], shape_one[1]) - self.assertEqual(shape_combined[1], shape_two[1]) + assert shape_combined[0] == shape_one[0] + shape_two[0] + assert shape_combined[1] == shape_one[1] + assert shape_combined[1] == shape_two[1] for key in EPISODE_TRAJ_KEYS: shape_one = traj_one[key].shape shape_two = traj_two[key].shape shape_combined = combined[key].shape - self.assertEqual(shape_combined[0], shape_one[0] + shape_two[0]) - - -if __name__ == "__main__": - unittest.main() + assert shape_combined[0] == shape_one[0] + shape_two[0] diff --git a/testing/planners_test.py b/testing/planners_test.py index 688b25fc..b66769bc 100644 --- a/testing/planners_test.py +++ b/testing/planners_test.py @@ -1,5 +1,3 @@ -import unittest - from overcooked_ai_py.agents.agent import AgentPair, GreedyHumanModel from overcooked_ai_py.agents.benchmarking import AgentEvaluator from overcooked_ai_py.mdp.actions import Action, Direction @@ -60,9 +58,6 @@ ) if large_mdp_tests: - # Not testing by default - - # Large MDP Setup large_mdp = OvercookedGridworld.from_layout_name("corridor", cook_time=5) no_counters_params = { @@ -98,9 +93,6 @@ custom_filename=action_manger_filename, force_compute=force_compute_large, ) - # Deprecated. - # hlam = HighLevelActionManager(ml_planner_large) - # hlp = HighLevelPlanner(hlam) def done_soup_obj(soup_loc, num_onion_inside=3): @@ -122,119 +114,116 @@ def soup_obj(soup_loc, num_onion_inside, cooking_tick): return SoupState(soup_loc, ingredient_obj_lst, cooking_tick) -class TestMotionPlanner(unittest.TestCase): - def test_gridworld_distance(self): - planner = ml_action_manager_simple.joint_motion_planner.motion_planner - start = ((2, 1), e) - end = ((1, 1), w) - dist = planner.get_gridworld_distance(start, end) - self.assertEqual(dist, 1) - - start = ((2, 1), e) - end = ((1, 1), n) - dist = planner.get_gridworld_distance(start, end) - self.assertEqual(dist, 2) - - start = (2, 1) - end = (1, 1) - dist = planner.get_gridworld_pos_distance(start, end) - self.assertEqual(dist, 1) - - start = (1, 1) - end = (3, 2) - dist = planner.get_gridworld_pos_distance(start, end) - self.assertEqual(dist, 3) +def _check_single_motion_plan( + motion_planner, + start_pos_and_or, + goal_pos_and_or, + expected_length=None, +): + start_state = OvercookedState( + [P(*start_pos_and_or), P((3, 2), n)], + {}, + all_orders=simple_mdp.start_all_orders, + ) + action_plan, pos_and_or_plan, plan_cost = motion_planner.get_plan( + start_pos_and_or, goal_pos_and_or + ) - def test_simple_mdp(self): - planner = ml_action_manager_simple.joint_motion_planner.motion_planner - self.simple_mdp_already_at_goal(planner) - self.simple_mdp_orientation_change(planner) - self.simple_mdp_basic_plan(planner) - self.simple_mdp_orientation_optimization_dependent_plans(planner) - - def simple_mdp_already_at_goal(self, planner): - start_status = goal_status = ((1, 1), n) - self.check_single_motion_plan( - planner, start_status, goal_status, expected_length=1 - ) + assert pos_and_or_plan[-1] == goal_pos_and_or - def simple_mdp_orientation_change(self, planner): - start_status = ((1, 1), n) - goal_status = ((1, 1), w) - self.check_single_motion_plan( - planner, start_status, goal_status, expected_length=2 - ) + graph_plan_cost = sum( + [motion_planner._graph_action_cost(a) for a in action_plan] + ) + assert plan_cost == graph_plan_cost + + joint_action_plan = [(a, stay) for a in action_plan] + env = OvercookedEnv.from_mdp(motion_planner.mdp, horizon=1000) + resulting_state, _ = env.execute_plan(start_state, joint_action_plan) + assert resulting_state.players_pos_and_or[0] == goal_pos_and_or + + if expected_length is not None: + assert len(action_plan) == expected_length + + +def _check_joint_plan( + joint_motion_planner, + start, + goal, + times=None, + min_t=None, + display=False, +): + ( + action_plan, + end_pos_and_orients, + plan_lengths, + ) = joint_motion_planner.get_low_level_action_plan(start, goal) + + start_state = OvercookedState( + [P(*start[0]), P(*start[1])], + {}, + all_orders=simple_mdp.start_all_orders, + ) + env = OvercookedEnv.from_mdp(joint_motion_planner.mdp, horizon=1000) + resulting_state, _ = env.execute_plan( + start_state, action_plan, display=display + ) - def simple_mdp_basic_plan(self, planner): - start_status = ((1, 1), n) - goal_status = ((3, 1), n) - self.check_single_motion_plan( - planner, start_status, goal_status, expected_length=4 - ) + assert any( + agent_goal in resulting_state.players_pos_and_or + for agent_goal in goal + ) + assert resulting_state.players_pos_and_or == end_pos_and_orients + assert len(action_plan) == min(plan_lengths) - def simple_mdp_orientation_optimization_dependent_plans(self, planner): - start_status = ((2, 1), n) - goal_status = ((1, 2), w) - self.check_single_motion_plan( - planner, start_status, goal_status, expected_length=3 - ) + if min_t is not None: + assert len(action_plan) == min_t + if times is not None: + assert plan_lengths == times - goal_status = ((1, 2), s) - self.check_single_motion_plan( - planner, start_status, goal_status, expected_length=3 - ) + +def _check_ml_action_manager(state, am, expected_mla_0, expected_mla_1): + player_0, player_1 = state.players + mla_0 = am.get_medium_level_actions(state, player_0) + mla_1 = am.get_medium_level_actions(state, player_1) + + assert set(mla_0) == set(expected_mla_0), ( + f"player 0's ml_action should be {expected_mla_0} but got {mla_0}" + ) + assert set(mla_1) == set(expected_mla_1), ( + f"player 1's ml_action should be {expected_mla_1} but got {mla_1}" + ) + + +class TestMotionPlanner: + def test_gridworld_distance(self): + planner = ml_action_manager_simple.joint_motion_planner.motion_planner + assert planner.get_gridworld_distance(((2, 1), e), ((1, 1), w)) == 1 + assert planner.get_gridworld_distance(((2, 1), e), ((1, 1), n)) == 2 + assert planner.get_gridworld_pos_distance((2, 1), (1, 1)) == 1 + assert planner.get_gridworld_pos_distance((1, 1), (3, 2)) == 3 + + def test_simple_mdp(self): + planner = ml_action_manager_simple.joint_motion_planner.motion_planner + # already at goal + _check_single_motion_plan(planner, ((1, 1), n), ((1, 1), n), expected_length=1) + # orientation change + _check_single_motion_plan(planner, ((1, 1), n), ((1, 1), w), expected_length=2) + # basic plan + _check_single_motion_plan(planner, ((1, 1), n), ((3, 1), n), expected_length=4) + # orientation optimization dependent plans + _check_single_motion_plan(planner, ((2, 1), n), ((1, 2), w), expected_length=3) + _check_single_motion_plan(planner, ((2, 1), n), ((1, 2), s), expected_length=3) def test_larger_mdp(self): if large_mdp_tests: planner = ( ml_planner_large.ml_action_manager.joint_motion_planner.motion_planner ) - self.large_mdp_basic_plan(planner) - - def large_mdp_basic_plan(self, planner): - start_status = ((1, 2), n) - goal_status = ((8, 1), n) - self.check_single_motion_plan(planner, start_status, goal_status) - - def check_single_motion_plan( - self, - motion_planner, - start_pos_and_or, - goal_pos_and_or, - expected_length=None, - ): - dummy_agent = P((3, 2), n) - start_state = OvercookedState( - [P(*start_pos_and_or), dummy_agent], - {}, - all_orders=simple_mdp.start_all_orders, - ) - action_plan, pos_and_or_plan, plan_cost = motion_planner.get_plan( - start_pos_and_or, goal_pos_and_or - ) - - # Checking that last state obtained matches goal position - self.assertEqual(pos_and_or_plan[-1], goal_pos_and_or) - - # In single motion plans the graph cost should be equal to - # the plan cost (= plan length) as agents should never STAY - graph_plan_cost = sum( - [motion_planner._graph_action_cost(a) for a in action_plan] - ) - self.assertEqual(plan_cost, graph_plan_cost) - - joint_action_plan = [(a, stay) for a in action_plan] - env = OvercookedEnv.from_mdp(motion_planner.mdp, horizon=1000) - resulting_state, _ = env.execute_plan(start_state, joint_action_plan) - self.assertEqual( - resulting_state.players_pos_and_or[0], goal_pos_and_or - ) - - if expected_length is not None: - self.assertEqual(len(action_plan), expected_length) + _check_single_motion_plan(planner, ((1, 2), n), ((8, 1), n)) -class TestJointMotionPlanner(unittest.TestCase): +class TestJointMotionPlanner: def test_same_start_and_end_pos_with_no_start_orientations(self): jm_planner = ml_action_manager_simple.joint_motion_planner start = (((1, 1), w), ((1, 2), s)) @@ -245,247 +234,51 @@ def test_same_start_and_end_pos_with_no_start_orientations(self): end_jm_state, finshing_times, ) = jm_planner.get_low_level_action_plan(start, goal) - optimal_plan = [(n, e), (interact, n)] - self.assertEqual(joint_action_plan, optimal_plan) - - optimal_end_jm_state = (((1, 1), n), ((2, 1), n)) - self.assertEqual(end_jm_state, optimal_end_jm_state) - - optimal_finshing_times = (2, 3) - self.assertEqual(finshing_times, optimal_finshing_times) + assert joint_action_plan == [(n, e), (interact, n)] + assert end_jm_state == (((1, 1), n), ((2, 1), n)) + assert finshing_times == (2, 3) def test_with_start_orientations_simple_mdp(self): jm_planner = or_ml_action_manager_simple.joint_motion_planner - self.simple_mdp_suite(jm_planner) + self._simple_mdp_suite(jm_planner) def test_without_start_orientations_simple_mdp(self): jm_planner = ml_action_manager_simple.joint_motion_planner - self.simple_mdp_suite(jm_planner) - - def simple_mdp_suite(self, jm_planner): - self.simple_mdp_already_at_goal(jm_planner) - self.simple_mdp_only_orientations_switch(jm_planner) - self.simple_mdp_one_at_goal(jm_planner) - self.simple_mdp_position_swap(jm_planner) - self.simple_mdp_one_at_goal_other_conflicting_path(jm_planner) - self.simple_mdp_test_final_orientation_optimization(jm_planner) - - def simple_mdp_already_at_goal(self, planner): - a1_start = a1_goal = ((1, 1), n) - a2_start = a2_goal = ((2, 1), n) - start = (a1_start, a2_start) - goal = (a1_goal, a2_goal) - self.check_joint_plan(planner, start, goal, times=(1, 1), min_t=1) - - a1_start = a1_goal = ((1, 1), w) - a2_start = a2_goal = ((1, 2), s) - start = (a1_start, a2_start) - goal = (a1_goal, a2_goal) - self.check_joint_plan(planner, start, goal, times=(1, 1), min_t=1) - - def simple_mdp_only_orientations_switch(self, planner): - a1_start = ((1, 1), s) - a1_goal = ((1, 1), w) - a2_start = ((1, 2), s) - a2_goal = ((1, 2), w) - start = (a1_start, a2_start) - goal = (a1_goal, a2_goal) - self.check_joint_plan(planner, start, goal, times=(2, 2), min_t=2) - - def simple_mdp_one_at_goal(self, planner): - a1_start = ((3, 2), s) - a1_goal = ((3, 2), s) - a2_start = ((2, 1), w) - a2_goal = ((1, 1), w) - start = (a1_start, a2_start) - goal = (a1_goal, a2_goal) - self.check_joint_plan(planner, start, goal, times=(1, 2)) - - def simple_mdp_position_swap(self, planner): - a1_start = ((1, 1), w) - a2_start = ((3, 2), s) - a1_goal = a2_start - a2_goal = a1_start - start = (a1_start, a2_start) - goal = (a1_goal, a2_goal) - self.check_joint_plan(planner, start, goal) - - def simple_mdp_one_at_goal_other_conflicting_path(self, planner): - a1_start = ((1, 1), w) - a1_goal = ((3, 1), e) - a2_start = a2_goal = ((2, 1), n) - start = (a1_start, a2_start) - goal = (a1_goal, a2_goal) - self.check_joint_plan(planner, start, goal, min_t=1) - - def simple_mdp_test_final_orientation_optimization(self, planner): + self._simple_mdp_suite(jm_planner) + + def _simple_mdp_suite(self, jm_planner): + # already at goal + _check_joint_plan(jm_planner, (((1, 1), n), ((2, 1), n)), (((1, 1), n), ((2, 1), n)), times=(1, 1), min_t=1) + _check_joint_plan(jm_planner, (((1, 1), w), ((1, 2), s)), (((1, 1), w), ((1, 2), s)), times=(1, 1), min_t=1) + # only orientations switch + _check_joint_plan(jm_planner, (((1, 1), s), ((1, 2), s)), (((1, 1), w), ((1, 2), w)), times=(2, 2), min_t=2) + # one at goal + _check_joint_plan(jm_planner, (((3, 2), s), ((2, 1), w)), (((3, 2), s), ((1, 1), w)), times=(1, 2)) + # position swap + _check_joint_plan(jm_planner, (((1, 1), w), ((3, 2), s)), (((3, 2), s), ((1, 1), w))) + # one at goal other conflicting path + _check_joint_plan(jm_planner, (((1, 1), w), ((2, 1), n)), (((3, 1), e), ((2, 1), n)), min_t=1) + # final orientation optimization a1_start = ((2, 1), n) - a1_goal = ((1, 2), w) a2_start = a2_goal = ((3, 2), s) - start = (a1_start, a2_start) - goal = (a1_goal, a2_goal) - # NOTE: Not considering all plans with same cost yet, this won't work - # check_joint_plan(planner, mdp, start, goal, times=(3, 1)) - a1_goal = ((1, 2), s) - start = (a1_start, a2_start) - goal = (a1_goal, a2_goal) - self.check_joint_plan(planner, start, goal, times=(3, 1)) + _check_joint_plan(jm_planner, (a1_start, a2_start), (a1_goal, a2_goal), times=(3, 1)) def test_large_mdp_suite_shared_motion_goals(self): if large_mdp_tests: jmp = ml_planner_large.ml_action_manager.joint_motion_planner - self.large_mdp_test_basic_plan(jmp) - self.large_mdp_test_shared_motion_goal(jmp) - self.large_mdp_test_shared_motion_goal_with_conflict(jmp) - self.large_mdp_test_shared_motion_goal_with_conflict_other(jmp) - - def large_mdp_test_basic_plan(self, planner): - a1_start = ((5, 1), n) - a2_start = ((8, 1), n) - a1_goal = a2_start - a2_goal = a1_start - start = (a1_start, a2_start) - goal = (a1_goal, a2_goal) - self.check_joint_plan(planner, start, goal) - - def large_mdp_test_shared_motion_goal(self, planner): - a1_start = ((4, 1), n) - a2_start = ((1, 1), n) - a1_goal = ((5, 1), n) - a2_goal = ((5, 1), n) - start = (a1_start, a2_start) - goal = (a1_goal, a2_goal) - self.check_joint_plan(planner, start, goal, min_t=3) - - def large_mdp_test_shared_motion_goal_with_conflict(self, planner): - assert planner.same_motion_goals - # When paths conflict for same goal, will resolve by making - # one agent wait (the one that results in the shortest plan) - a1_start = ((5, 2), n) - a2_start = ((4, 1), n) - a1_goal = ((5, 1), n) - a2_goal = ((5, 1), n) - start = (a1_start, a2_start) - goal = (a1_goal, a2_goal) - self.check_joint_plan(planner, start, goal, min_t=2) - - def large_mdp_test_shared_motion_goal_with_conflict_other(self, planner): - assert planner.same_motion_goals - a1_start = ((4, 2), e) - a2_start = ((4, 1), e) - a1_goal = ((5, 1), n) - a2_goal = ((5, 1), n) - start = (a1_start, a2_start) - goal = (a1_goal, a2_goal) - self.check_joint_plan(planner, start, goal, min_t=3) - - def check_joint_plan( - self, - joint_motion_planner, - start, - goal, - times=None, - min_t=None, - display=False, - ): - """Runs the plan in the environment and checks that the intended goals are achieved.""" - debug = False - ( - action_plan, - end_pos_and_orients, - plan_lengths, - ) = joint_motion_planner.get_low_level_action_plan(start, goal) - if debug: - print( - "Start state: {}, Goal state: {}, Action plan: {}".format( - start, goal, action_plan - ) - ) - - start_state = OvercookedState( - [P(*start[0]), P(*start[1])], - {}, - all_orders=simple_mdp.start_all_orders, - ) - env = OvercookedEnv.from_mdp(joint_motion_planner.mdp, horizon=1000) - resulting_state, _ = env.execute_plan( - start_state, action_plan, display=display - ) - - self.assertTrue( - any( - [ - agent_goal in resulting_state.players_pos_and_or - for agent_goal in goal - ] - ) - ) - self.assertEqual( - resulting_state.players_pos_and_or, end_pos_and_orients - ) - self.assertEqual(len(action_plan), min(plan_lengths)) - - if min_t is not None: - self.assertEqual(len(action_plan), min_t) - if times is not None: - self.assertEqual(plan_lengths, times) - - -# Rewritten because the previous test depended on Heuristic, and Heuristic has been deprecated -class TestMediumLevelActionManagerSimple(unittest.TestCase): - def test_simple_mdp_without_start_orientations(self): - print("Simple - no start orientations (& shared motion goals)") - mlam = ml_action_manager_simple - self.simple_mpd_empty_hands(mlam) - self.simple_mdp_deliver_soup(mlam) - self.simple_mdp_pickup_counter_soup(mlam) - self.simple_mdp_pickup_counter_dish(mlam) - self.simple_mdp_pickup_counter_onion(mlam) - self.simple_mdp_drop_useless_dish_with_soup_idle(mlam) - self.simple_mdp_pickup_soup(mlam) - self.simple_mdp_pickup_dish(mlam) - self.simple_mdp_start_good_soup_cooking(mlam) - self.simple_mdp_start_bad_soup_cooking(mlam) - self.simple_mdp_start_1_onion_soup_cooking(mlam) - self.simple_mdp_drop_useless_onion_good_soup(mlam) - self.simple_mdp_drop_useless_onion_bad_soup(mlam) - self.simple_mdp_add_3rd_onion(mlam) - self.simple_mdp_add_2nd_onion(mlam) - self.simple_mdp_drop_useless_dish(mlam) - - def test_simple_mdp_with_start_orientations(self): - print("Simple - with start orientations (no shared motion goals)") - mlam = or_ml_action_manager_simple - self.simple_mpd_empty_hands(mlam, counter_drop_forbidden=True) - self.simple_mdp_deliver_soup(mlam, counter_drop_forbidden=True) - self.simple_mdp_pickup_counter_soup(mlam, counter_drop_forbidden=True) - self.simple_mdp_pickup_counter_dish(mlam, counter_drop_forbidden=True) - self.simple_mdp_pickup_counter_onion(mlam, counter_drop_forbidden=True) - self.simple_mdp_drop_useless_dish_with_soup_idle( - mlam, counter_drop_forbidden=True - ) - self.simple_mdp_pickup_soup(mlam, counter_drop_forbidden=True) - self.simple_mdp_pickup_dish(mlam, counter_drop_forbidden=True) - self.simple_mdp_start_good_soup_cooking( - mlam, counter_drop_forbidden=True - ) - self.simple_mdp_start_bad_soup_cooking( - mlam, counter_drop_forbidden=True - ) - self.simple_mdp_start_1_onion_soup_cooking( - mlam, counter_drop_forbidden=True - ) - self.simple_mdp_drop_useless_onion_good_soup( - mlam, counter_drop_forbidden=True - ) - self.simple_mdp_drop_useless_onion_bad_soup( - mlam, counter_drop_forbidden=True - ) - self.simple_mdp_add_3rd_onion(mlam, counter_drop_forbidden=True) - self.simple_mdp_add_2nd_onion(mlam, counter_drop_forbidden=True) - self.simple_mdp_drop_useless_dish(mlam, counter_drop_forbidden=True) - + # basic plan + _check_joint_plan(jmp, (((5, 1), n), ((8, 1), n)), (((8, 1), n), ((5, 1), n))) + # shared motion goal + _check_joint_plan(jmp, (((4, 1), n), ((1, 1), n)), (((5, 1), n), ((5, 1), n)), min_t=3) + # shared motion goal with conflict + assert jmp.same_motion_goals + _check_joint_plan(jmp, (((5, 2), n), ((4, 1), n)), (((5, 1), n), ((5, 1), n)), min_t=2) + # shared motion goal with conflict other + _check_joint_plan(jmp, (((4, 2), e), ((4, 1), e)), (((5, 1), n), ((5, 1), n)), min_t=3) + + +class TestMediumLevelActionManagerSimple: ONION_PICKUP = ((3, 2), (1, 0)) DISH_PICKUP = ((2, 2), (0, 1)) COUNTER_DROP = ((1, 1), (0, -1)) @@ -493,333 +286,219 @@ def test_simple_mdp_with_start_orientations(self): POT_INTERACT = ((2, 1), (00, -1)) SOUP_DELIVER = ((3, 2), (0, 1)) - def simple_mpd_empty_hands(self, planner, counter_drop_forbidden=False): + def test_simple_mdp_without_start_orientations(self): + mlam = ml_action_manager_simple + self._run_all_scenarios(mlam, counter_drop_forbidden=False) + + def test_simple_mdp_with_start_orientations(self): + mlam = or_ml_action_manager_simple + self._run_all_scenarios(mlam, counter_drop_forbidden=True) + + def _run_all_scenarios(self, planner, counter_drop_forbidden): + self._empty_hands(planner, counter_drop_forbidden) + self._deliver_soup(planner, counter_drop_forbidden) + self._pickup_counter_soup(planner, counter_drop_forbidden) + self._pickup_counter_dish(planner, counter_drop_forbidden) + self._pickup_counter_onion(planner, counter_drop_forbidden) + self._drop_useless_dish_with_soup_idle(planner, counter_drop_forbidden) + self._pickup_soup(planner, counter_drop_forbidden) + self._pickup_dish(planner, counter_drop_forbidden) + self._start_good_soup_cooking(planner, counter_drop_forbidden) + self._start_bad_soup_cooking(planner, counter_drop_forbidden) + self._start_1_onion_soup_cooking(planner, counter_drop_forbidden) + self._drop_useless_onion_good_soup(planner, counter_drop_forbidden) + self._drop_useless_onion_bad_soup(planner, counter_drop_forbidden) + self._add_3rd_onion(planner, counter_drop_forbidden) + self._add_2nd_onion(planner, counter_drop_forbidden) + self._drop_useless_dish(planner, counter_drop_forbidden) + + def _empty_hands(self, planner, counter_drop_forbidden): s = OvercookedState( - [P((2, 2), n), P((2, 1), n)], - {}, + [P((2, 2), n), P((2, 1), n)], {}, all_orders=simple_mdp.start_all_orders, ) - self.check_ml_action_manager( - s, - planner, + _check_ml_action_manager( + s, planner, [self.ONION_PICKUP, self.DISH_PICKUP], [self.ONION_PICKUP, self.DISH_PICKUP], ) - def simple_mdp_deliver_soup(self, planner, counter_drop_forbidden=False): + def _deliver_soup(self, planner, counter_drop_forbidden): s = OvercookedState( - [P((2, 2), n), P((2, 1), n, done_soup_obj((2, 1)))], - {}, + [P((2, 2), n), P((2, 1), n, done_soup_obj((2, 1)))], {}, all_orders=simple_mdp.start_all_orders, ) - if counter_drop_forbidden: - self.check_ml_action_manager( - s, - planner, - [self.ONION_PICKUP, self.DISH_PICKUP], - [self.SOUP_DELIVER], - ) + _check_ml_action_manager(s, planner, + [self.ONION_PICKUP, self.DISH_PICKUP], [self.SOUP_DELIVER]) else: - self.check_ml_action_manager( - s, - planner, - [self.ONION_PICKUP, self.DISH_PICKUP], - [self.COUNTER_DROP, self.SOUP_DELIVER], - ) + _check_ml_action_manager(s, planner, + [self.ONION_PICKUP, self.DISH_PICKUP], [self.COUNTER_DROP, self.SOUP_DELIVER]) - def simple_mdp_pickup_counter_soup( - self, planner, counter_drop_forbidden=False - ): + def _pickup_counter_soup(self, planner, counter_drop_forbidden): s = OvercookedState( [P((2, 2), n), P((2, 1), n)], {(0, 2): done_soup_obj((0, 2))}, all_orders=simple_mdp.start_all_orders, ) - self.check_ml_action_manager( - s, - planner, - [self.ONION_PICKUP, self.DISH_PICKUP, self.COUNTER_PICKUP], + _check_ml_action_manager(s, planner, [self.ONION_PICKUP, self.DISH_PICKUP, self.COUNTER_PICKUP], - ) + [self.ONION_PICKUP, self.DISH_PICKUP, self.COUNTER_PICKUP]) - def simple_mdp_pickup_counter_dish( - self, planner, counter_drop_forbidden=False - ): + def _pickup_counter_dish(self, planner, counter_drop_forbidden): s = OvercookedState( [P((2, 2), n), P((2, 1), n)], {(0, 2): Obj("dish", (0, 2))}, all_orders=simple_mdp.start_all_orders, ) - self.check_ml_action_manager( - s, - planner, + _check_ml_action_manager(s, planner, [self.ONION_PICKUP, self.DISH_PICKUP, self.COUNTER_PICKUP], - [self.ONION_PICKUP, self.DISH_PICKUP, self.COUNTER_PICKUP], - ) + [self.ONION_PICKUP, self.DISH_PICKUP, self.COUNTER_PICKUP]) - def simple_mdp_pickup_counter_onion( - self, planner, counter_drop_forbidden=False - ): + def _pickup_counter_onion(self, planner, counter_drop_forbidden): s = OvercookedState( [P((2, 2), n), P((2, 1), n)], {(0, 2): Obj("onion", (0, 2))}, all_orders=simple_mdp.start_all_orders, ) - self.check_ml_action_manager( - s, - planner, - [self.ONION_PICKUP, self.DISH_PICKUP, self.COUNTER_PICKUP], + _check_ml_action_manager(s, planner, [self.ONION_PICKUP, self.DISH_PICKUP, self.COUNTER_PICKUP], - ) + [self.ONION_PICKUP, self.DISH_PICKUP, self.COUNTER_PICKUP]) - def simple_mdp_drop_useless_dish_with_soup_idle( - self, planner, counter_drop_forbidden=False - ): + def _drop_useless_dish_with_soup_idle(self, planner, counter_drop_forbidden): s = OvercookedState( [P((2, 2), n), P((2, 1), n, Obj("dish", (2, 1)))], {(2, 0): idle_soup_obj((2, 0), 3)}, all_orders=simple_mdp.start_all_orders, ) if counter_drop_forbidden: - self.check_ml_action_manager( - s, - planner, - [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], - [], - ) + _check_ml_action_manager(s, planner, + [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], []) else: - self.check_ml_action_manager( - s, - planner, - [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], - [self.COUNTER_DROP], - ) + _check_ml_action_manager(s, planner, + [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], [self.COUNTER_DROP]) - def simple_mdp_pickup_soup(self, planner, counter_drop_forbidden=False): + def _pickup_soup(self, planner, counter_drop_forbidden): s = OvercookedState( [P((2, 2), n), P((2, 1), n, Obj("dish", (2, 1)))], {(2, 0): done_soup_obj((2, 0))}, all_orders=simple_mdp.start_all_orders, ) if counter_drop_forbidden: - self.check_ml_action_manager( - s, - planner, - [self.ONION_PICKUP, self.DISH_PICKUP], - [self.POT_INTERACT], - ) + _check_ml_action_manager(s, planner, + [self.ONION_PICKUP, self.DISH_PICKUP], [self.POT_INTERACT]) else: - self.check_ml_action_manager( - s, - planner, - [self.ONION_PICKUP, self.DISH_PICKUP], - [self.COUNTER_DROP, self.POT_INTERACT], - ) + _check_ml_action_manager(s, planner, + [self.ONION_PICKUP, self.DISH_PICKUP], [self.COUNTER_DROP, self.POT_INTERACT]) - def simple_mdp_pickup_dish(self, planner, counter_drop_forbidden=False): + def _pickup_dish(self, planner, counter_drop_forbidden): s = OvercookedState( [P((2, 2), n), P((2, 1), n)], {(2, 0): done_soup_obj((2, 0))}, all_orders=simple_mdp.start_all_orders, ) - self.check_ml_action_manager( - s, - planner, + _check_ml_action_manager(s, planner, [self.ONION_PICKUP, self.DISH_PICKUP], - [self.ONION_PICKUP, self.DISH_PICKUP], - ) + [self.ONION_PICKUP, self.DISH_PICKUP]) - def simple_mdp_start_good_soup_cooking( - self, planner, counter_drop_forbidden=False - ): + def _start_good_soup_cooking(self, planner, counter_drop_forbidden): s = OvercookedState( [P((2, 2), n), P((2, 1), n)], {(2, 0): idle_soup_obj((2, 0), 3)}, all_orders=simple_mdp.start_all_orders, ) - self.check_ml_action_manager( - s, - planner, + _check_ml_action_manager(s, planner, [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], - [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], - ) + [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT]) - def simple_mdp_start_bad_soup_cooking( - self, planner, counter_drop_forbidden=False - ): + def _start_bad_soup_cooking(self, planner, counter_drop_forbidden): s = OvercookedState( [P((2, 2), n), P((2, 1), n)], {(2, 0): idle_soup_obj((2, 0), 2)}, all_orders=simple_mdp.start_all_orders, ) - self.check_ml_action_manager( - s, - planner, - [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], + _check_ml_action_manager(s, planner, [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], - ) + [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT]) - def simple_mdp_start_1_onion_soup_cooking( - self, planner, counter_drop_forbidden=False - ): + def _start_1_onion_soup_cooking(self, planner, counter_drop_forbidden): s = OvercookedState( [P((2, 2), n), P((2, 1), n)], {(2, 0): idle_soup_obj((2, 0), 1)}, all_orders=simple_mdp.start_all_orders, ) - self.check_ml_action_manager( - s, - planner, - [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], + _check_ml_action_manager(s, planner, [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], - ) + [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT]) - def simple_mdp_drop_useless_onion_good_soup( - self, planner, counter_drop_forbidden=False - ): + def _drop_useless_onion_good_soup(self, planner, counter_drop_forbidden): s = OvercookedState( [P((2, 2), n), P((2, 1), n, Obj("onion", (2, 1)))], {(2, 0): done_soup_obj((2, 0))}, all_orders=simple_mdp.start_all_orders, ) if counter_drop_forbidden: - self.check_ml_action_manager( - s, planner, [self.ONION_PICKUP, self.DISH_PICKUP], [] - ) + _check_ml_action_manager(s, planner, + [self.ONION_PICKUP, self.DISH_PICKUP], []) else: - self.check_ml_action_manager( - s, - planner, - [self.ONION_PICKUP, self.DISH_PICKUP], - [self.COUNTER_DROP], - ) + _check_ml_action_manager(s, planner, + [self.ONION_PICKUP, self.DISH_PICKUP], [self.COUNTER_DROP]) - def simple_mdp_drop_useless_onion_bad_soup( - self, planner, counter_drop_forbidden=False - ): + def _drop_useless_onion_bad_soup(self, planner, counter_drop_forbidden): s = OvercookedState( [P((2, 2), n), P((2, 1), n, Obj("onion", (2, 1)))], {(2, 0): done_soup_obj((2, 0), 2)}, all_orders=simple_mdp.start_all_orders, ) if counter_drop_forbidden: - self.check_ml_action_manager( - s, planner, [self.ONION_PICKUP, self.DISH_PICKUP], [] - ) + _check_ml_action_manager(s, planner, + [self.ONION_PICKUP, self.DISH_PICKUP], []) else: - self.check_ml_action_manager( - s, - planner, - [self.ONION_PICKUP, self.DISH_PICKUP], - [self.COUNTER_DROP], - ) + _check_ml_action_manager(s, planner, + [self.ONION_PICKUP, self.DISH_PICKUP], [self.COUNTER_DROP]) - def simple_mdp_add_3rd_onion(self, planner, counter_drop_forbidden=False): + def _add_3rd_onion(self, planner, counter_drop_forbidden): s = OvercookedState( [P((2, 2), n), P((2, 1), n, Obj("onion", (2, 1)))], {(2, 0): idle_soup_obj((2, 0), 2)}, all_orders=simple_mdp.start_all_orders, ) if counter_drop_forbidden: - self.check_ml_action_manager( - s, - planner, - [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], - [self.POT_INTERACT], - ) + _check_ml_action_manager(s, planner, + [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], [self.POT_INTERACT]) else: - self.check_ml_action_manager( - s, - planner, - [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], - [self.COUNTER_DROP, self.POT_INTERACT], - ) + _check_ml_action_manager(s, planner, + [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], [self.COUNTER_DROP, self.POT_INTERACT]) - def simple_mdp_add_2nd_onion(self, planner, counter_drop_forbidden=False): + def _add_2nd_onion(self, planner, counter_drop_forbidden): s = OvercookedState( [P((2, 2), n), P((2, 1), n, Obj("onion", (2, 1)))], {(2, 0): idle_soup_obj((2, 0), 1)}, all_orders=simple_mdp.start_all_orders, ) if counter_drop_forbidden: - self.check_ml_action_manager( - s, - planner, - [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], - [self.POT_INTERACT], - ) + _check_ml_action_manager(s, planner, + [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], [self.POT_INTERACT]) else: - self.check_ml_action_manager( - s, - planner, - [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], - [self.COUNTER_DROP, self.POT_INTERACT], - ) + _check_ml_action_manager(s, planner, + [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], [self.COUNTER_DROP, self.POT_INTERACT]) - def simple_mdp_drop_useless_dish( - self, planner, counter_drop_forbidden=False - ): + def _drop_useless_dish(self, planner, counter_drop_forbidden): s = OvercookedState( [P((2, 2), n), P((2, 1), n, Obj("dish", (2, 1)))], {(2, 0): idle_soup_obj((2, 0), 1)}, all_orders=simple_mdp.start_all_orders, ) if counter_drop_forbidden: - self.check_ml_action_manager( - s, - planner, - [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], - [self.POT_INTERACT], - ) + _check_ml_action_manager(s, planner, + [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], [self.POT_INTERACT]) else: - self.check_ml_action_manager( - s, - planner, - [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], - [self.COUNTER_DROP, self.POT_INTERACT], - ) - - def check_ml_action_manager( - self, state, am, expected_mla_0, expected_mla_1, debug=False - ): - """ - args: - state (OvercookedState): an overcooked state - am (MediumLevelActionManager): the planer whose action manager will be tested - - This function checks if all the mid-level actions make sense for each player state inside STATE - """ - player_0, player_1 = state.players - - mla_0 = am.get_medium_level_actions(state, player_0) - mla_1 = am.get_medium_level_actions(state, player_1) - - if debug: - print("Player 0 mla", mla_0) - print("Player 1 mla", mla_1) - print(am.mdp.state_string(state)) - - self.assertEqual( - set(mla_0), - set(expected_mla_0), - "player 0's ml_action should be " - + str(expected_mla_0) - + " but get " - + str(mla_0), - ) - self.assertEqual( - set(mla_1), - set(expected_mla_1), - "player 0's ml_action should be " - + str(expected_mla_1) - + " but get " - + str(mla_1), - ) + _check_ml_action_manager(s, planner, + [self.ONION_PICKUP, self.DISH_PICKUP, self.POT_INTERACT], [self.COUNTER_DROP, self.POT_INTERACT]) -class TestScenarios(unittest.TestCase): - def repetative_runs(self, evaluator, num_games=10): +class TestScenarios: + def _repetitive_runs(self, evaluator, num_games=10): trajectory_0 = evaluator.evaluate_human_model_pair( num_games=num_games, native_eval=True ) @@ -842,17 +521,6 @@ def repetative_runs(self, evaluator, num_games=10): ) def test_scenario_3_no_counter(self): - # Asymmetric advantage scenario - # - # X X X X X O X X X X - # S X X P X - # X ↑H X - # D X X X X!X X X - # X →R O - # X X X X X X X X X X - # - # This test does not allow counter by using the default NO_COUNTER_PARAMS when calling from_layout_name - mdp_params = {"layout_name": "scenario3"} mdp = OvercookedGridworld.from_layout_name(**mdp_params) start_state = mdp.get_standard_start_state() @@ -861,21 +529,9 @@ def test_scenario_3_no_counter(self): eva = AgentEvaluator.from_layout_name( mdp_params, env_params, force_compute=force_compute ) - - self.repetative_runs(eva) + self._repetitive_runs(eva) def test_scenario_3_yes_counter(self): - # Asymmetric advantage scenario - # - # X X X X X O X X X X - # S X X P X - # X ↑H X - # D X X X X!X X X - # X →R O - # X X X X X X X X X X - # - # This test does not allow only (5. 3) as the only counter - mdp_params = {"layout_name": "scenario3"} mdp = OvercookedGridworld.from_layout_name(**mdp_params) start_state = mdp.get_standard_start_state() @@ -897,34 +553,4 @@ def test_scenario_3_yes_counter(self): mlam_params=one_counter_params, force_compute=force_compute, ) - - self.repetative_runs(eva) - - -# # Deprecated. because of Heuristic -# class TestHighLevelPlanner(unittest.TestCase): -# """The HighLevelPlanner class has been mostly discontinued""" -# -# def test_basic_hl_planning(self): -# if large_mdp_tests: -# s = OvercookedState( -# [P((2, 2), n), -# P((2, 1), n)], -# {}, order_list=[]) -# h = Heuristic(hlp.mp) -# hlp.get_hl_plan(s, h.simple_heuristic) -# -# s = OvercookedState( -# [P((2, 2), n), -# P((2, 1), n)], -# {}, order_list=['any', 'any', 'any']) -# -# hlp.get_low_level_action_plan(s, h.simple_heuristic) -# # hlp.get_low_level_action_plan(s, h.hard_heuristic) -# -# # heuristic = Heuristic(ml_planner_large.mp) -# # ml_planner_large.get_low_level_action_plan(s, heuristic.simple_heuristic) -# # ml_planner_large.get_low_level_action_plan(s, heuristic.hard_heuristic) - -if __name__ == "__main__": - unittest.main() + self._repetitive_runs(eva) diff --git a/testing/test_gym_env_warnings.py b/testing/test_gym_env_warnings.py new file mode 100644 index 00000000..cf028f1e --- /dev/null +++ b/testing/test_gym_env_warnings.py @@ -0,0 +1,125 @@ +import warnings + +import gymnasium +import numpy as np +import pytest + +from overcooked_ai_py.mdp.overcooked_env import OvercookedEnv +from overcooked_ai_py.mdp.overcooked_mdp import OvercookedGridworld + + +@pytest.fixture +def env(): + base_mdp = OvercookedGridworld.from_layout_name("cramped_room") + base_env = OvercookedEnv.from_mdp(base_mdp, horizon=100) + env = gymnasium.make( + "Overcooked-v0", + base_env=base_env, + featurize_fn=base_env.featurize_state_mdp, + ) + yield env + env.close() + + +class TestNoGymnasiumWarnings: + def test_reset_no_warnings(self, env): + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always") + env.reset() + gym_warnings = [ + w for w in caught if "gymnasium" in (w.filename or "").lower() + ] + assert gym_warnings == [], ( + f"Gymnasium warnings during reset(): " + f"{[str(w.message) for w in gym_warnings]}" + ) + + def test_step_no_warnings(self, env): + env.reset() + action = (0, 0) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always") + env.step(action) + gym_warnings = [ + w for w in caught if "gymnasium" in (w.filename or "").lower() + ] + assert gym_warnings == [], ( + f"Gymnasium warnings during step(): " + f"{[str(w.message) for w in gym_warnings]}" + ) + + +class TestObservationFormat: + def test_reset_returns_tuple_of_arrays(self, env): + obs, info = env.reset() + assert isinstance(obs, tuple), f"Expected tuple, got {type(obs)}" + assert len(obs) == 2 + for i, ob in enumerate(obs): + assert isinstance(ob, np.ndarray), ( + f"obs[{i}] should be np.ndarray, got {type(ob)}" + ) + + def test_step_returns_tuple_of_arrays(self, env): + env.reset() + obs, reward, done, truncated, info = env.step((0, 0)) + assert isinstance(obs, tuple), f"Expected tuple, got {type(obs)}" + assert len(obs) == 2 + for i, ob in enumerate(obs): + assert isinstance(ob, np.ndarray), ( + f"obs[{i}] should be np.ndarray, got {type(ob)}" + ) + + def test_obs_matches_observation_space(self, env): + obs, _ = env.reset() + assert env.observation_space.contains(obs), ( + "reset() obs not in observation_space" + ) + + def test_step_obs_matches_observation_space(self, env): + env.reset() + obs, _, _, _, _ = env.step((0, 0)) + assert env.observation_space.contains(obs), ( + "step() obs not in observation_space" + ) + + def test_obs_shape_matches_featurize(self, env): + obs, _ = env.reset() + expected_shape = env.unwrapped.mdp.get_featurize_state_shape() + assert obs[0].shape == expected_shape + assert obs[1].shape == expected_shape + + def test_obs_dtype(self, env): + obs, _ = env.reset() + assert np.issubdtype(obs[0].dtype, np.floating) + + +class TestInfoDict: + def test_reset_info_has_overcooked_state(self, env): + _, info = env.reset() + assert "overcooked_state" in info + + def test_reset_info_has_other_agent_env_idx(self, env): + _, info = env.reset() + assert "other_agent_env_idx" in info + assert info["other_agent_env_idx"] in (0, 1) + + def test_step_info_has_overcooked_state(self, env): + env.reset() + _, _, _, _, info = env.step((0, 0)) + assert "overcooked_state" in info + + def test_step_info_has_other_agent_env_idx(self, env): + env.reset() + _, _, _, _, info = env.step((0, 0)) + assert "other_agent_env_idx" in info + assert info["other_agent_env_idx"] in (0, 1) + + +class TestObservationSpace: + def test_is_tuple_space(self, env): + assert isinstance(env.observation_space, gymnasium.spaces.Tuple) + assert len(env.observation_space.spaces) == 2 + + def test_tuple_contains_box_spaces(self, env): + for space in env.observation_space.spaces: + assert isinstance(space, gymnasium.spaces.Box) diff --git a/testing/visualization_test.py b/testing/visualization_test.py index 80f16f99..39924956 100644 --- a/testing/visualization_test.py +++ b/testing/visualization_test.py @@ -1,10 +1,10 @@ import copy import json import os -import unittest import numpy as np import pygame +import pytest from overcooked_ai_py.agents.agent import RandomAgent from overcooked_ai_py.agents.benchmarking import AgentEvaluator @@ -29,13 +29,12 @@ def get_file_count(directory_path): ) -def test_render_state_from_dict(test_dict): +def _check_render_state_from_dict(test_dict): input_dict = copy.deepcopy(test_dict) test_dict = copy.deepcopy(test_dict) test_dict["kwargs"]["state"] = OvercookedState.from_dict( test_dict["kwargs"]["state"] ) - # check only if it raise error or not, for image fidelity render_state check is used StateVisualizer(**test_dict["config"]).display_rendered_state( img_path=example_img_path, **test_dict["kwargs"] ) @@ -96,10 +95,12 @@ def test_render_state_from_dict(test_dict): return True -class TestStateVisualizer(unittest.TestCase): - def setUp(self): - Recipe.configure({}) +@pytest.fixture(autouse=True) +def _configure_recipe(): + Recipe.configure({}) + +class TestStateVisualizer: def test_setting_up_configs(self): default_values = copy.deepcopy(StateVisualizer.DEFAULT_VALUES) @@ -114,37 +115,32 @@ def test_setting_up_configs(self): ) visualizer = StateVisualizer(**init_config) - self.assertEqual(init_config["tile_size"], visualizer.tile_size) + assert init_config["tile_size"] == visualizer.tile_size visualizer.configure(**configure_config) - self.assertEqual(configure_config["tile_size"], visualizer.tile_size) + assert configure_config["tile_size"] == visualizer.tile_size StateVisualizer.configure_defaults(**configure_defaults_config) - self.assertEqual( - configure_defaults_config["tile_size"], - StateVisualizer.DEFAULT_VALUES["tile_size"], - ) - self.assertEqual( - configure_defaults_config["tile_size"], StateVisualizer().tile_size + assert ( + configure_defaults_config["tile_size"] + == StateVisualizer.DEFAULT_VALUES["tile_size"] ) + assert configure_defaults_config["tile_size"] == StateVisualizer().tile_size invalid_kwargs = {"invalid_argument": 123} - self.assertRaises(AssertionError, StateVisualizer, **invalid_kwargs) - self.assertRaises( - AssertionError, - StateVisualizer.configure_defaults, - **invalid_kwargs - ) - self.assertRaises( - AssertionError, visualizer.configure, **invalid_kwargs - ) + with pytest.raises(AssertionError): + StateVisualizer(**invalid_kwargs) + with pytest.raises(AssertionError): + StateVisualizer.configure_defaults(**invalid_kwargs) + with pytest.raises(AssertionError): + visualizer.configure(**invalid_kwargs) def test_properties(self): visualizer = StateVisualizer( tile_size=30, hud_interline_size=7, hud_font_size=26 ) - self.assertEqual(visualizer.scale_by_factor, 2) - self.assertEqual(visualizer.hud_line_height, 26 + 7) + assert visualizer.scale_by_factor == 2 + assert visualizer.hud_line_height == 26 + 7 def test_hud_display(self): for d in load_from_json( @@ -152,7 +148,7 @@ def test_hud_display(self): state_visualizer_dir, "render_state_data_test_hud.json" ) ): - test_render_state_from_dict(d) + _check_render_state_from_dict(d) def test_differnet_sizes(self): for d in load_from_json( @@ -160,7 +156,7 @@ def test_differnet_sizes(self): state_visualizer_dir, "render_state_data_test_sizes.json" ) ): - test_render_state_from_dict(d) + _check_render_state_from_dict(d) def test_cooking_timer_display(self): for d in load_from_json( @@ -169,7 +165,7 @@ def test_cooking_timer_display(self): "render_state_data_test_cooking_display.json", ) ): - test_render_state_from_dict(d) + _check_render_state_from_dict(d) def test_various_states(self): for d in load_from_json( @@ -177,7 +173,7 @@ def test_various_states(self): state_visualizer_dir, "render_state_data_test_various.json" ) ): - test_render_state_from_dict(d) + _check_render_state_from_dict(d) def test_generated_layout_states(self): for d in load_from_json( @@ -186,12 +182,12 @@ def test_generated_layout_states(self): "render_state_data_test_generated_layout.json", ) ): - test_render_state_from_dict(d) + _check_render_state_from_dict(d) def test_default_hud_data_from_trajectories(self): traj_path = os.path.join( TESTING_DATA_DIR, "test_state_visualizer", "test_trajectory.json" - ) # NOTE: for test purposes reward is added here despite there was no soup delivery in trajectory + ) test_trajectory = AgentEvaluator.load_traj_from_json(traj_path) hud_data_path = os.path.join( TESTING_DATA_DIR, @@ -202,9 +198,8 @@ def test_default_hud_data_from_trajectories(self): result_hud_data = StateVisualizer().default_hud_data_from_trajectories( test_trajectory ) - self.assertEqual( - json.dumps(result_hud_data, sort_keys=True), - json.dumps(expected_hud_data, sort_keys=True), + assert json.dumps(result_hud_data, sort_keys=True) == json.dumps( + expected_hud_data, sort_keys=True ) def test_action_probs_display(self): @@ -214,10 +209,9 @@ def test_action_probs_display(self): "render_state_data_test_action_probs_display.json", ) ): - test_render_state_from_dict(d) + _check_render_state_from_dict(d) def test_trajectory_visualization(self): - # we don't have good way to check slider automatically so its mostly test for basic stuff like numer of outputed images, if using method raises error etc. traj_path = os.path.join( TESTING_DATA_DIR, "test_state_visualizer", "test_trajectory.json" ) @@ -237,16 +231,12 @@ def test_trajectory_visualization(self): ipython_display=False, ) ) - self.assertEqual( - get_file_count(result_img_directory_path), expected_images_num - ) + assert get_file_count(result_img_directory_path) == expected_images_num custom_img_directory_path = generate_temporary_file_path( prefix="overcooked_visualized_trajectory", extension="" ) - self.assertNotEqual( - custom_img_directory_path, result_img_directory_path - ) + assert custom_img_directory_path != result_img_directory_path result_img_directory_path = ( StateVisualizer().display_rendered_trajectory( test_trajectory, @@ -254,11 +244,5 @@ def test_trajectory_visualization(self): ipython_display=False, ) ) - self.assertEqual(custom_img_directory_path, result_img_directory_path) - self.assertEqual( - get_file_count(result_img_directory_path), expected_images_num - ) - - -if __name__ == "__main__": - unittest.main() + assert custom_img_directory_path == result_img_directory_path + assert get_file_count(result_img_directory_path) == expected_images_num diff --git a/uv.lock b/uv.lock index f8503f5c..193ae6f1 100644 --- a/uv.lock +++ b/uv.lock @@ -1,38 +1,49 @@ version = 1 -requires-python = "==3.10.*" +revision = 3 +requires-python = ">=3.13" resolution-markers = [ - "platform_machine == 'x86_64' and sys_platform == 'darwin'", - "platform_machine != 'x86_64' and sys_platform == 'darwin'", - "platform_machine == 'aarch64' and sys_platform == 'linux'", - "platform_machine != 'aarch64' and sys_platform == 'linux'", - "sys_platform != 'darwin' and sys_platform != 'linux'", + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and 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temporary fork of [HumanCompatibleAI/overcooked_ai](https://github.com/HumanCompatibleAI/overcooked_ai), with full credit to [Micah Carroll](https://github.com/micahcarroll) and team for the original work. The purpose of this fork is to bring all Python dependencies up to date (Python 3.13, numpy 2.x, gymnasium, etc.) and modernize the test framework. We will close this fork if/when these changes can be merged into the original repository. + +--- + ![MDP python tests](https://github.com/HumanCompatibleAI/overcooked_ai/workflows/.github/workflows/pythontests.yml/badge.svg) ![overcooked-ai codecov](https://codecov.io/gh/HumanCompatibleAI/overcooked_ai/branch/master/graph/badge.svg) [![PyPI version](https://badge.fury.io/py/overcooked-ai.svg)](https://badge.fury.io/py/overcooked-ai) [!["Open Issues"](https://img.shields.io/github/issues-raw/HumanCompatibleAI/overcooked_ai.svg)](https://github.com/HumanCompatibleAI/minerl/overcooked_ai) [![GitHub issues by-label](https://img.shields.io/github/issues-raw/HumanCompatibleAI/overcooked_ai/bug.svg?color=red)](https://github.com/HumanCompatibleAI/overcooked_ai/issues?utf8=%E2%9C%93&q=is%3Aissue+is%3Aopen+label%3Abug) [![Downloads](https://pepy.tech/badge/overcooked-ai)](https://pepy.tech/project/overcooked-ai) [![arXiv](https://img.shields.io/badge/arXiv-1910.05789-bbbbbb.svg)](https://arxiv.org/abs/1910.05789)