Fix: Need to supply seed to reset in tests
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9605f2e56c
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fbba129034
@ -78,7 +78,7 @@ def test_missing_local_state(mp_type: str):
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{'controller_type': 'motor'},
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{'phase_generator_type': 'exp'},
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{'basis_generator_type': basis_generator_type})
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env.reset()
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env.reset(seed=SEED)
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with pytest.raises(NotImplementedError):
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env.step(env.action_space.sample())
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@ -95,7 +95,7 @@ def test_verbosity(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]]
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{'controller_type': 'motor'},
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{'phase_generator_type': 'exp'},
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{'basis_generator_type': basis_generator_type})
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env.reset()
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env.reset(seed=SEED)
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_obs, _reward, _terminated, _truncated, info = env.step(env.action_space.sample())
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info_keys = list(info.keys())
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@ -125,7 +125,7 @@ def test_length(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]]):
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{'basis_generator_type': basis_generator_type})
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for i in range(5):
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env.reset()
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env.reset(seed=SEED)
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_obs, _reward, _terminated, _truncated, info = env.step(env.action_space.sample())
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length = info['trajectory_length']
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@ -141,7 +141,7 @@ def test_aggregation(mp_type: str, reward_aggregation: Callable[[np.ndarray], fl
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{'controller_type': 'motor'},
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{'phase_generator_type': 'exp'},
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{'basis_generator_type': basis_generator_type})
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env.reset()
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env.reset(seed=SEED)
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# ToyEnv only returns 1 as reward
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_obs, reward, _terminated, _truncated, _info = env.step(env.action_space.sample())
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assert reward == reward_aggregation(np.ones(50, ))
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@ -232,7 +232,7 @@ def test_learn_tau(mp_type: str, tau: float):
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done = True
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for i in range(5):
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if done:
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env.reset()
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env.reset(seed=SEED)
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action = env.action_space.sample()
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action[0] = tau
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@ -278,7 +278,7 @@ def test_learn_delay(mp_type: str, delay: float):
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done = True
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for i in range(5):
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if done:
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env.reset()
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env.reset(seed=SEED)
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action = env.action_space.sample()
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action[0] = delay
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@ -327,7 +327,7 @@ def test_learn_tau_and_delay(mp_type: str, tau: float, delay: float):
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done = True
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for i in range(5):
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if done:
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env.reset()
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env.reset(seed=SEED)
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action = env.action_space.sample()
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action[0] = tau
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action[1] = delay
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@ -7,6 +7,7 @@ import numpy as np
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import pytest
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from gymnasium import register
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from gymnasium.core import ActType, ObsType
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from gymnasium import spaces
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import fancy_gym
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from fancy_gym.black_box.raw_interface_wrapper import RawInterfaceWrapper
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@ -85,7 +86,7 @@ def test_learn_sub_trajectories(mp_type: str, env_wrap: Tuple[str, Type[RawInter
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for i in range(25):
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if done:
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env.reset()
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env.reset(seed=SEED)
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action = env.action_space.sample()
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_obs, _reward, terminated, truncated, info = env.step(action)
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done = terminated or truncated
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@ -131,7 +132,7 @@ def test_replanning_time(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWra
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# This also verifies we are not adding the TimeAwareObservationWrapper twice
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assert env.observation_space == env_step.observation_space
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env.reset()
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env.reset(seed=SEED)
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episode_steps = env_step.spec.max_episode_steps // replanning_time
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# Make 3 episodes, total steps depend on the replanning steps
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@ -146,7 +147,7 @@ def test_replanning_time(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWra
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# Check if number of steps until termination match the replanning interval
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print(done, (i + 1), episode_steps)
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assert (i + 1) % episode_steps == 0
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env.reset()
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env.reset(seed=SEED)
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assert replanning_schedule(None, None, None, None, length)
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@ -171,7 +172,7 @@ def test_max_planning_times(mp_type: str, max_planning_times: int, sub_segment_s
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{'basis_generator_type': basis_generator_type,
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},
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seed=SEED)
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_ = env.reset()
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_ = env.reset(seed=SEED)
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done = False
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planning_times = 0
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while not done:
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@ -203,7 +204,7 @@ def test_replanning_with_learn_tau(mp_type: str, max_planning_times: int, sub_se
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{'basis_generator_type': basis_generator_type,
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},
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seed=SEED)
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_ = env.reset()
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_ = env.reset(seed=SEED)
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done = False
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planning_times = 0
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while not done:
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@ -236,7 +237,7 @@ def test_replanning_with_learn_delay(mp_type: str, max_planning_times: int, sub_
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{'basis_generator_type': basis_generator_type,
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},
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seed=SEED)
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_ = env.reset()
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_ = env.reset(seed=SEED)
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done = False
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planning_times = 0
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while not done:
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@ -291,7 +292,7 @@ def test_replanning_with_learn_delay_and_tau(mp_type: str, max_planning_times: i
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{'basis_generator_type': basis_generator_type,
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},
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seed=SEED)
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_ = env.reset()
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_ = env.reset(seed=SEED)
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done = False
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planning_times = 0
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while not done:
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@ -340,7 +341,7 @@ def test_replanning_schedule(mp_type: str, max_planning_times: int, sub_segment_
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{'basis_generator_type': basis_generator_type,
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},
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seed=SEED)
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_ = env.reset()
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_ = env.reset(seed=SEED)
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for i in range(max_planning_times):
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action = env.action_space.sample()
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_obs, _reward, terminated, truncated, _info = env.step(action)
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@ -30,7 +30,7 @@ def run_env(env_id: str, iterations: int = None, seed: int = 0, wrappers: List[T
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actions = []
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terminations = []
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truncations = []
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obs, _ = env.reset()
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obs, _ = env.reset(seed=seed)
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verify_observations(obs, env.observation_space, "reset()")
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iterations = iterations or (env.spec.max_episode_steps or 1)
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