updated test to pytest

fancy_gym/black_box/black_box_wrapper.py

@@ -77,7 +77,6 @@ class BlackBoxWrapper(gym.ObservationWrapper):
         # TODO: is this correct for replanning? Do we need to adjust anything here?
         bc_time = np.array(0 if not self.do_replanning else self.current_traj_steps * self.dt)
         self.traj_gen.set_boundary_conditions(bc_time, self.current_pos, self.current_vel)
-        # TODO: remove the - self.dt after Bruces fix.
         self.traj_gen.set_duration(None if self.learn_sub_trajectories else self.duration, self.dt)
         # traj_dict = self.traj_gen.get_trajs(get_pos=True, get_vel=True)
         trajectory = get_numpy(self.traj_gen.get_traj_pos())

test/test_dmc.py

@@ -1,130 +0,0 @@
-import unittest
-
-import gym
-import numpy as np
-from dm_control import suite, manipulation
-
-import fancy_gym
-from fancy_gym import make
-
-SUITE_IDS = [f'dmc:{env}-{task}' for env, task in suite.ALL_TASKS if env != "lqr"]
-MANIPULATION_IDS = [f'dmc:manipulation-{task}' for task in manipulation.ALL if task.endswith('_features')]
-SEED = 1
-
-
-class TestDMCEnvironments(unittest.TestCase):
-
-    def _run_env(self, env_id, iterations=None, seed=SEED, render=False):
-        """
-        Example for running a DMC based env in the step based setting.
-        The env_id has to be specified as `dmc:domain_name-task_name` or
-        for manipulation tasks as `manipulation-environment_name`
-
-        Args:
-            env_id: Either `dmc:domain_name-task_name` or `dmc:manipulation-environment_name`
-            iterations: Number of rollout steps to run
-            seed: random seeding
-            render: Render the episode
-
-        Returns: observations, rewards, dones, actions
-
-        """
-        env: gym.Env = make(env_id, seed=seed)
-        rewards = []
-        observations = []
-        actions = []
-        dones = []
-        obs = env.reset()
-        self._verify_observations(obs, env.observation_space, "reset()")
-
-        iterations = iterations or (env.spec.max_episode_steps or 1)
-
-        # number of samples(multiple environment steps)
-        for i in range(iterations):
-            observations.append(obs)
-
-            ac = env.action_space.sample()
-            actions.append(ac)
-            # ac = np.random.uniform(env.action_space.low, env.action_space.high, env.action_space.shape)
-            obs, reward, done, info = env.step(ac)
-
-            self._verify_observations(obs, env.observation_space, "step()")
-            self._verify_reward(reward)
-            self._verify_done(done)
-
-            rewards.append(reward)
-            dones.append(done)
-
-            if render:
-                env.render("human")
-
-            if done:
-                break
-
-        assert done, "Done flag is not True after end of episode."
-        observations.append(obs)
-        env.close()
-        del env
-        return np.array(observations), np.array(rewards), np.array(dones), np.array(actions)
-
-    def _run_env_determinism(self, ids):
-        seed = 0
-        for env_id in ids:
-            with self.subTest(msg=env_id):
-                traj1 = self._run_env(env_id, seed=seed)
-                traj2 = self._run_env(env_id, seed=seed)
-                for i, time_step in enumerate(zip(*traj1, *traj2)):
-                    obs1, rwd1, done1, ac1, obs2, rwd2, done2, ac2 = time_step
-                    self.assertTrue(np.array_equal(obs1, obs2), f"Observations [{i}] delta {obs1 - obs2} is not zero.")
-                    self.assertTrue(np.array_equal(ac1, ac2), f"Actions [{i}] delta {ac1 - ac2} is not zero.")
-                    self.assertEqual(done1, done2, f"Dones [{i}] {done1} and {done2} do not match.")
-                    self.assertEqual(rwd1, rwd2, f"Rewards [{i}] {rwd1} and {rwd2} do not match.")
-
-    def _verify_observations(self, obs, observation_space, obs_type="reset()"):
-        self.assertTrue(observation_space.contains(obs),
-                        f"Observation {obs} received from {obs_type} "
-                        f"not contained in observation space {observation_space}.")
-
-    def _verify_reward(self, reward):
-        self.assertIsInstance(reward, (float, int), f"Returned type {type(reward)} as reward, expected float or int.")
-
-    def _verify_done(self, done):
-        self.assertIsInstance(done, bool, f"Returned {done} as done flag, expected bool.")
-
-    def test_suite_functionality(self):
-        """Tests that suite step environments run without errors using random actions."""
-        for env_id in SUITE_IDS:
-            with self.subTest(msg=env_id):
-                self._run_env(env_id)
-
-    def test_suite_determinism(self):
-        """Tests that for step environments identical seeds produce identical trajectories."""
-        self._run_env_determinism(SUITE_IDS)
-
-    def test_manipulation_functionality(self):
-        """Tests that manipulation step environments run without errors using random actions."""
-        for env_id in MANIPULATION_IDS:
-            with self.subTest(msg=env_id):
-                self._run_env(env_id)
-
-    def test_manipulation_determinism(self):
-        """Tests that for step environments identical seeds produce identical trajectories."""
-        self._run_env_determinism(MANIPULATION_IDS)
-
-    def test_bb_functionality(self):
-        """Tests that black box environments run without errors using random actions."""
-        for traj_gen, env_ids in fancy_gym.ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS.items():
-            with self.subTest(msg=traj_gen):
-                for id in env_ids:
-                    with self.subTest(msg=id):
-                        self._run_env(id)
-
-    def test_bb_determinism(self):
-        """Tests that for black box environment identical seeds produce identical trajectories."""
-        for traj_gen, env_ids in fancy_gym.ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS.items():
-            with self.subTest(msg=traj_gen):
-                self._run_env_determinism(env_ids)
-
-
-if __name__ == '__main__':
-    unittest.main()

test/test_dmc_envs.py

@@ -0,0 +1,48 @@
+from itertools import chain
+
+import pytest
+from dm_control import suite, manipulation
+
+import fancy_gym
+from test.utils import run_env, run_env_determinism
+
+SUITE_IDS = [f'dmc:{env}-{task}' for env, task in suite.ALL_TASKS if env != "lqr"]
+MANIPULATION_IDS = [f'dmc:manipulation-{task}' for task in manipulation.ALL if task.endswith('_features')]
+DMC_MP_IDS = chain(*fancy_gym.ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS.values())
+SEED = 1
+
+
+@pytest.mark.parametrize('env_id', SUITE_IDS)
+def test_step_suite_functionality(env_id: str):
+    """Tests that suite step environments run without errors using random actions."""
+    run_env(env_id)
+
+
+@pytest.mark.parametrize('env_id', SUITE_IDS)
+def test_step_suite_determinism(env_id: str):
+    """Tests that for step environments identical seeds produce identical trajectories."""
+    run_env_determinism(env_id, SEED)
+
+
+@pytest.mark.parametrize('env_id', MANIPULATION_IDS)
+def test_step_manipulation_functionality(env_id: str):
+    """Tests that manipulation step environments run without errors using random actions."""
+    run_env(env_id)
+
+
+@pytest.mark.parametrize('env_id', MANIPULATION_IDS)
+def test_step_manipulation_determinism(env_id: str):
+    """Tests that for step environments identical seeds produce identical trajectories."""
+    run_env_determinism(env_id, SEED)
+
+
+@pytest.mark.parametrize('env_id', DMC_MP_IDS)
+def test_bb_dmc_functionality(env_id: str):
+    """Tests that black box environments run without errors using random actions."""
+    run_env(env_id)
+
+
+@pytest.mark.parametrize('env_id', DMC_MP_IDS)
+def test_bb_dmc_determinism(env_id: str):
+    """Tests that for black box environment identical seeds produce identical trajectories."""
+    run_env_determinism(env_id, SEED)

test/test_fancy.py

@@ -1,118 +0,0 @@
-import unittest
-
-import gym
-import numpy as np
-
-import fancy_gym  # noqa
-from fancy_gym.utils.make_env_helpers import make
-
-CUSTOM_IDS = [spec.id for spec in gym.envs.registry.all() if
-              "fancy_gym" in spec.entry_point and 'make_bb_env_helper' not in spec.entry_point]
-SEED = 1
-
-
-class TestCustomEnvironments(unittest.TestCase):
-
-    def _run_env(self, env_id, iterations=None, seed=SEED, render=False):
-        """
-        Example for running a DMC based env in the step based setting.
-        The env_id has to be specified as `domain_name-task_name` or
-        for manipulation tasks as `manipulation-environment_name`
-
-        Args:
-            env_id: Either `domain_name-task_name` or `manipulation-environment_name`
-            iterations: Number of rollout steps to run
-            seed: random seeding
-            render: Render the episode
-
-        Returns: observations, rewards, dones, actions
-
-        """
-        env: gym.Env = make(env_id, seed=seed)
-        rewards = []
-        actions = []
-        observations = []
-        dones = []
-        obs = env.reset()
-        self._verify_observations(obs, env.observation_space, "reset()")
-
-        iterations = iterations or (env.spec.max_episode_steps or 1)
-
-        # number of samples(multiple environment steps)
-        for i in range(iterations):
-            observations.append(obs)
-
-            ac = env.action_space.sample()
-            actions.append(ac)
-            obs, reward, done, info = env.step(ac)
-
-            self._verify_observations(obs, env.observation_space, "step()")
-            self._verify_reward(reward)
-            self._verify_done(done)
-
-            rewards.append(reward)
-            dones.append(done)
-
-            if render:
-                env.render("human")
-
-            if done:
-                break
-
-        assert done, "Done flag is not True after end of episode."
-        observations.append(obs)
-        env.close()
-        del env
-        return np.array(observations), np.array(rewards), np.array(dones), np.array(actions)
-
-    def _run_env_determinism(self, ids):
-        seed = 0
-        for env_id in ids:
-            with self.subTest(msg=env_id):
-                traj1 = self._run_env(env_id, seed=seed)
-                traj2 = self._run_env(env_id, seed=seed)
-                for i, time_step in enumerate(zip(*traj1, *traj2)):
-                    obs1, rwd1, done1, ac1, obs2, rwd2, done2, ac2 = time_step
-                    self.assertTrue(np.array_equal(ac1, ac2), f"Actions [{i}] delta {ac1 - ac2} is not zero.")
-                    self.assertTrue(np.array_equal(obs1, obs2), f"Observations [{i}] delta {obs1 - obs2} is not zero.")
-                    self.assertEqual(rwd1, rwd2, f"Rewards [{i}] {rwd1} and {rwd2} do not match.")
-                    self.assertEqual(done1, done2, f"Dones [{i}] {done1} and {done2} do not match.")
-
-    def _verify_observations(self, obs, observation_space, obs_type="reset()"):
-        self.assertTrue(observation_space.contains(obs),
-                        f"Observation {obs} received from {obs_type} "
-                        f"not contained in observation space {observation_space}.")
-
-    def _verify_reward(self, reward):
-        self.assertIsInstance(reward, (float, int), f"Returned type {type(reward)} as reward, expected float or int.")
-
-    def _verify_done(self, done):
-        self.assertIsInstance(done, bool, f"Returned {done} as done flag, expected bool.")
-
-    def test_step_functionality(self):
-        """Tests that step environments run without errors using random actions."""
-        for env_id in CUSTOM_IDS:
-            with self.subTest(msg=env_id):
-                self._run_env(env_id)
-
-    def test_step_determinism(self):
-        """Tests that for step environments identical seeds produce identical trajectories."""
-        self._run_env_determinism(CUSTOM_IDS)
-
-    def test_bb_functionality(self):
-        """Tests that black box environments run without errors using random actions."""
-        for traj_gen, env_ids in fancy_gym.ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS.items():
-            with self.subTest(msg=traj_gen):
-                for id in env_ids:
-                    with self.subTest(msg=id):
-                        self._run_env(id)
-
-    def test_bb_determinism(self):
-        """Tests that for black box environment identical seeds produce identical trajectories."""
-        for traj_gen, env_ids in fancy_gym.ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS.items():
-            with self.subTest(msg=traj_gen):
-                self._run_env_determinism(env_ids)
-
-
-if __name__ == '__main__':
-    unittest.main()

test/test_fancy_envs.py

@@ -1,3 +1,5 @@
+import itertools
+
 import fancy_gym
 import gym
 import pytest
@@ -6,30 +8,29 @@ from test.utils import run_env, run_env_determinism
 
 CUSTOM_IDS = [spec.id for spec in gym.envs.registry.all() if
               "fancy_gym" in spec.entry_point and 'make_bb_env_helper' not in spec.entry_point]
+CUSTOM_MP_IDS = itertools.chain(*fancy_gym.ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS.values())
 SEED = 1
 
 
 @pytest.mark.parametrize('env_id', CUSTOM_IDS)
-def test_step_functionality(env_id: str):
+def test_step_fancy_functionality(env_id: str):
     """Tests that step environments run without errors using random actions."""
     run_env(env_id)
 
 
 @pytest.mark.parametrize('env_id', CUSTOM_IDS)
-def test_step_determinism(env_id: str):
+def test_step_fancy_determinism(env_id: str):
     """Tests that for step environments identical seeds produce identical trajectories."""
-    seed = 0
+    run_env_determinism(env_id, SEED)
-    run_env_determinism(env_id, seed)
 
 
-@pytest.mark.parametrize('env_id', fancy_gym.ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS.values())
+@pytest.mark.parametrize('env_id', CUSTOM_MP_IDS)
-def test_bb_functionality(env_id: str):
+def test_bb_fancy_functionality(env_id: str):
     """Tests that black box environments run without errors using random actions."""
     run_env(env_id)
 
 
-@pytest.mark.parametrize('env_id', fancy_gym.ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS.values())
+@pytest.mark.parametrize('env_id', CUSTOM_MP_IDS)
-def test_bb_determinism(env_id: str):
+def test_bb_fancy_determinism(env_id: str):
     """Tests that for black box environment identical seeds produce identical trajectories."""
-    seed = 0
+    run_env_determinism(env_id, SEED)
-    run_env_determinism(env_id, seed)

test/test_gym.py

@@ -1,118 +0,0 @@
-import unittest
-
-import gym
-import numpy as np
-
-import fancy_gym
-from fancy_gym import make
-
-GYM_IDS = [spec.id for spec in gym.envs.registry.all() if
-           "fancy_gym" not in spec.entry_point and 'make_bb_env_helper' not in spec.entry_point]
-SEED = 1
-
-
-class TestGymEnvironments(unittest.TestCase):
-
-    def _run_env(self, env_id, iterations=None, seed=SEED, render=False):
-        """
-        Example for running a openai gym env in the step based setting.
-        The env_id has to be specified as `env_id-vX`.
-
-        Args:
-            env_id: env id in the form `env_id-vX`
-            iterations: Number of rollout steps to run
-            seed: random seeding
-            render: Render the episode
-
-        Returns:
-
-        """
-        env: gym.Env = make(env_id, seed=seed)
-        rewards = []
-        observations = []
-        actions = []
-        dones = []
-        obs = env.reset()
-        self._verify_observations(obs, env.observation_space, "reset()")
-
-        iterations = iterations or (env.spec.max_episode_steps or 1)
-
-        # number of samples(multiple environment steps)
-        for i in range(iterations):
-            observations.append(obs)
-
-            ac = env.action_space.sample()
-            actions.append(ac)
-            # ac = np.random.uniform(env.action_space.low, env.action_space.high, env.action_space.shape)
-            obs, reward, done, info = env.step(ac)
-
-            self._verify_observations(obs, env.observation_space, "step()")
-            self._verify_reward(reward)
-            self._verify_done(done)
-
-            rewards.append(reward)
-            dones.append(done)
-
-            if render:
-                env.render("human")
-
-            if done:
-                break
-
-        assert done or env.spec.max_episode_steps is None, "Done flag is not True after end of episode."
-        observations.append(obs)
-        env.close()
-        del env
-        return np.array(observations), np.array(rewards), np.array(dones), np.array(actions)
-
-    def _run_env_determinism(self, ids):
-        seed = 0
-        for env_id in ids:
-            with self.subTest(msg=env_id):
-                traj1 = self._run_env(env_id, seed=seed)
-                traj2 = self._run_env(env_id, seed=seed)
-                for i, time_step in enumerate(zip(*traj1, *traj2)):
-                    obs1, rwd1, done1, ac1, obs2, rwd2, done2, ac2 = time_step
-                    self.assertTrue(np.array_equal(ac1, ac2), f"Actions [{i}] delta {ac1 - ac2} is not zero.")
-                    self.assertTrue(np.array_equal(obs1, obs2), f"Observations [{i}] delta {obs1 - obs2} is not zero.")
-                    self.assertEqual(rwd1, rwd2, f"Rewards [{i}] {rwd1} and {rwd2} do not match.")
-                    self.assertEqual(done1, done2, f"Dones [{i}] {done1} and {done2} do not match.")
-
-    def _verify_observations(self, obs, observation_space, obs_type="reset()"):
-        self.assertTrue(observation_space.contains(obs),
-                        f"Observation {obs} received from {obs_type} "
-                        f"not contained in observation space {observation_space}.")
-
-    def _verify_reward(self, reward):
-        self.assertIsInstance(reward, (float, int), f"Returned type {type(reward)} as reward, expected float or int.")
-
-    def _verify_done(self, done):
-        self.assertIsInstance(done, bool, f"Returned {done} as done flag, expected bool.")
-
-    def test_step_functionality(self):
-        """Tests that step environments run without errors using random actions."""
-        for env_id in GYM_IDS:
-            with self.subTest(msg=env_id):
-                self._run_env(env_id)
-
-    def test_step_determinism(self):
-        """Tests that for step environments identical seeds produce identical trajectories."""
-        self._run_env_determinism(GYM_IDS)
-
-    def test_bb_functionality(self):
-        """Tests that black box environments run without errors using random actions."""
-        for traj_gen, env_ids in fancy_gym.ALL_GYM_MOVEMENT_PRIMITIVE_ENVIRONMENTS.items():
-            with self.subTest(msg=traj_gen):
-                for id in env_ids:
-                    with self.subTest(msg=id):
-                        self._run_env(id)
-
-    def test_bb_determinism(self):
-        """Tests that for black box environment identical seeds produce identical trajectories."""
-        for traj_gen, env_ids in fancy_gym.ALL_GYM_MOVEMENT_PRIMITIVE_ENVIRONMENTS.items():
-            with self.subTest(msg=traj_gen):
-                self._run_env_determinism(env_ids)
-
-
-if __name__ == '__main__':
-    unittest.main()

test/test_gym_envs.py

@@ -0,0 +1,36 @@
+from itertools import chain
+
+import gym
+import pytest
+
+import fancy_gym
+from test.utils import run_env, run_env_determinism
+
+GYM_IDS = [spec.id for spec in gym.envs.registry.all() if
+           "fancy_gym" not in spec.entry_point and 'make_bb_env_helper' not in spec.entry_point]
+GYM_MP_IDS = chain(*fancy_gym.ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS.values())
+SEED = 1
+
+
+@pytest.mark.parametrize('env_id', GYM_IDS)
+def test_step_gym_functionality(env_id: str):
+    """Tests that step environments run without errors using random actions."""
+    run_env(env_id)
+
+
+@pytest.mark.parametrize('env_id', GYM_IDS)
+def test_step_gym_determinism(env_id: str):
+    """Tests that for step environments identical seeds produce identical trajectories."""
+    run_env_determinism(env_id, SEED)
+
+
+@pytest.mark.parametrize('env_id', GYM_MP_IDS)
+def test_bb_gym_functionality(env_id: str):
+    """Tests that black box environments run without errors using random actions."""
+    run_env(env_id)
+
+
+@pytest.mark.parametrize('env_id', GYM_MP_IDS)
+def test_bb_gym_determinism(env_id: str):
+    """Tests that for black box environment identical seeds produce identical trajectories."""
+    run_env_determinism(env_id, SEED)

test/test_metaworld.py

@@ -1,119 +0,0 @@
-import unittest
-
-import gym
-import numpy as np
-from metaworld.envs import ALL_V2_ENVIRONMENTS_GOAL_OBSERVABLE
-
-import fancy_gym
-from fancy_gym import make
-
-METAWORLD_IDS = [f'metaworld:{env.split("-goal-observable")[0]}' for env, _ in
-                 ALL_V2_ENVIRONMENTS_GOAL_OBSERVABLE.items()]
-SEED = 1
-
-
-class TestMetaWorldEnvironments(unittest.TestCase):
-
-    def _run_env(self, env_id, iterations=None, seed=SEED, render=False):
-        """
-        Example for running a metaworld based env in the step based setting.
-        The env_id has to be specified as `metaworld:env_id-vX`.
-
-        Args:
-            env_id: env id in the form `metaworld:env_id-vX`
-            iterations: Number of rollout steps to run
-            seed: random seeding
-            render: Render the episode
-
-        Returns:
-
-        """
-        env: gym.Env = make(env_id, seed=seed)
-        rewards = []
-        observations = []
-        actions = []
-        dones = []
-        obs = env.reset()
-        self._verify_observations(obs, env.observation_space, "reset()")
-
-        iterations = iterations or (env.spec.max_episode_steps or 1)
-
-        # number of samples(multiple environment steps)
-        for i in range(iterations):
-            observations.append(obs)
-
-            ac = env.action_space.sample()
-            actions.append(ac)
-            # ac = np.random.uniform(env.action_space.low, env.action_space.high, env.action_space.shape)
-            obs, reward, done, info = env.step(ac)
-
-            self._verify_observations(obs, env.observation_space, "step()")
-            self._verify_reward(reward)
-            self._verify_done(done)
-
-            rewards.append(reward)
-            dones.append(done)
-
-            if render:
-                env.render("human")
-
-            if done:
-                break
-
-        assert done, "Done flag is not True after end of episode."
-        observations.append(obs)
-        env.close()
-        del env
-        return np.array(observations), np.array(rewards), np.array(dones), np.array(actions)
-
-    def _run_env_determinism(self, ids):
-        seed = 0
-        for env_id in ids:
-            with self.subTest(msg=env_id):
-                traj1 = self._run_env(env_id, seed=seed)
-                traj2 = self._run_env(env_id, seed=seed)
-                for i, time_step in enumerate(zip(*traj1, *traj2)):
-                    obs1, rwd1, done1, ac1, obs2, rwd2, done2, ac2 = time_step
-                    self.assertTrue(np.array_equal(ac1, ac2), f"Actions [{i}] delta {ac1 - ac2} is not zero.")
-                    self.assertTrue(np.array_equal(obs1, obs2), f"Observations [{i}] delta {obs1 - obs2} is not zero.")
-                    self.assertEqual(rwd1, rwd2, f"Rewards [{i}] {rwd1} and {rwd2} do not match.")
-                    self.assertEqual(done1, done2, f"Dones [{i}] {done1} and {done2} do not match.")
-
-    def _verify_observations(self, obs, observation_space, obs_type="reset()"):
-        self.assertTrue(observation_space.contains(obs),
-                        f"Observation {obs} received from {obs_type} "
-                        f"not contained in observation space {observation_space}.")
-
-    def _verify_reward(self, reward):
-        self.assertIsInstance(reward, (float, int), f"Returned type {type(reward)} as reward, expected float or int.")
-
-    def _verify_done(self, done):
-        self.assertIsInstance(done, bool, f"Returned {done} as done flag, expected bool.")
-
-    def test_step_functionality(self):
-        """Tests that step environments run without errors using random actions."""
-        for env_id in METAWORLD_IDS:
-            with self.subTest(msg=env_id):
-                self._run_env(env_id)
-
-    def test_step_determinism(self):
-        """Tests that for step environments identical seeds produce identical trajectories."""
-        self._run_env_determinism(METAWORLD_IDS)
-
-    def test_bb_functionality(self):
-        """Tests that black box environments run without errors using random actions."""
-        for traj_gen, env_ids in fancy_gym.ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS.items():
-            with self.subTest(msg=traj_gen):
-                for id in env_ids:
-                    with self.subTest(msg=id):
-                        self._run_env(id)
-
-    def test_bb_determinism(self):
-        """Tests that for black box environment identical seeds produce identical trajectories."""
-        for traj_gen, env_ids in fancy_gym.ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS.items():
-            with self.subTest(msg=traj_gen):
-                self._run_env_determinism(env_ids)
-
-
-if __name__ == '__main__':
-    unittest.main()

test/test_metaworld_envs.py

@@ -0,0 +1,37 @@
+from itertools import chain
+
+import pytest
+from metaworld.envs import ALL_V2_ENVIRONMENTS_GOAL_OBSERVABLE
+
+import fancy_gym
+from test.utils import run_env, run_env_determinism
+
+METAWORLD_IDS = [f'metaworld:{env.split("-goal-observable")[0]}' for env, _ in
+                 ALL_V2_ENVIRONMENTS_GOAL_OBSERVABLE.items()]
+METAWORLD_MP_IDS = chain(*fancy_gym.ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS.values())
+print(METAWORLD_MP_IDS)
+SEED = 1
+
+
+@pytest.mark.parametrize('env_id', METAWORLD_IDS)
+def test_step_metaworld_functionality(env_id: str):
+    """Tests that step environments run without errors using random actions."""
+    run_env(env_id)
+
+
+@pytest.mark.parametrize('env_id', METAWORLD_IDS)
+def test_step_metaworld_determinism(env_id: str):
+    """Tests that for step environments identical seeds produce identical trajectories."""
+    run_env_determinism(env_id, SEED)
+
+
+@pytest.mark.parametrize('env_id', METAWORLD_MP_IDS)
+def test_bb_metaworld_functionality(env_id: str):
+    """Tests that black box environments run without errors using random actions."""
+    run_env(env_id)
+
+
+@pytest.mark.parametrize('env_id', METAWORLD_MP_IDS)
+def test_bb_metaworld_determinism(env_id: str):
+    """Tests that for black box environment identical seeds produce identical trajectories."""
+    run_env_determinism(env_id, SEED)

test/utils.py

@@ -69,7 +69,7 @@ def run_env_determinism(env_id: str, seed: int):
         assert np.array_equal(done1, done2), f"Dones [{i}] {done1} and {done2} do not match."
 
 
-def verify_observations(obs, observation_space, obs_type="reset()"):
+def verify_observations(obs, observation_space: gym.Space, obs_type="reset()"):
     assert observation_space.contains(obs), \
         f"Observation {obs} received from {obs_type} not contained in observation space {observation_space}."