Fix: Need to supply seed to reset in tests

test/test_black_box.py

@@ -78,7 +78,7 @@ def test_missing_local_state(mp_type: str):
                             {'controller_type': 'motor'},
                             {'phase_generator_type': 'exp'},
                             {'basis_generator_type': basis_generator_type})
-    env.reset()
+    env.reset(seed=SEED)
     with pytest.raises(NotImplementedError):
         env.step(env.action_space.sample())
 
@@ -95,7 +95,7 @@ def test_verbosity(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]]
                             {'controller_type': 'motor'},
                             {'phase_generator_type': 'exp'},
                             {'basis_generator_type': basis_generator_type})
-    env.reset()
+    env.reset(seed=SEED)
     _obs, _reward, _terminated, _truncated, info = env.step(env.action_space.sample())
     info_keys = list(info.keys())
 
@@ -125,7 +125,7 @@ def test_length(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]]):
                             {'basis_generator_type': basis_generator_type})
 
     for i in range(5):
-        env.reset()
+        env.reset(seed=SEED)
         _obs, _reward, _terminated, _truncated, info = env.step(env.action_space.sample())
         length = info['trajectory_length']
 
@@ -141,7 +141,7 @@ def test_aggregation(mp_type: str, reward_aggregation: Callable[[np.ndarray], fl
                             {'controller_type': 'motor'},
                             {'phase_generator_type': 'exp'},
                             {'basis_generator_type': basis_generator_type})
-    env.reset()
+    env.reset(seed=SEED)
     # ToyEnv only returns 1 as reward
     _obs, reward, _terminated, _truncated, _info = env.step(env.action_space.sample())
     assert reward == reward_aggregation(np.ones(50, ))
@@ -232,7 +232,7 @@ def test_learn_tau(mp_type: str, tau: float):
     done = True
     for i in range(5):
         if done:
-            env.reset()
+            env.reset(seed=SEED)
         action = env.action_space.sample()
         action[0] = tau
 
@@ -278,7 +278,7 @@ def test_learn_delay(mp_type: str, delay: float):
     done = True
     for i in range(5):
         if done:
-            env.reset()
+            env.reset(seed=SEED)
         action = env.action_space.sample()
         action[0] = delay
 
@@ -327,7 +327,7 @@ def test_learn_tau_and_delay(mp_type: str, tau: float, delay: float):
     done = True
     for i in range(5):
         if done:
-            env.reset()
+            env.reset(seed=SEED)
         action = env.action_space.sample()
         action[0] = tau
         action[1] = delay

test/test_replanning_sequencing.py

@@ -7,6 +7,7 @@ import numpy as np
 import pytest
 from gymnasium import register
 from gymnasium.core import ActType, ObsType
+from gymnasium import spaces
 
 import fancy_gym
 from fancy_gym.black_box.raw_interface_wrapper import RawInterfaceWrapper
@@ -85,7 +86,7 @@ def test_learn_sub_trajectories(mp_type: str, env_wrap: Tuple[str, Type[RawInter
 
     for i in range(25):
         if done:
-            env.reset()
+            env.reset(seed=SEED)
         action = env.action_space.sample()
         _obs, _reward, terminated, truncated, info = env.step(action)
         done = terminated or truncated
@@ -131,7 +132,7 @@ def test_replanning_time(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWra
     # This also verifies we are not adding the TimeAwareObservationWrapper twice
     assert env.observation_space == env_step.observation_space
 
-    env.reset()
+    env.reset(seed=SEED)
 
     episode_steps = env_step.spec.max_episode_steps // replanning_time
     # Make 3 episodes, total steps depend on the replanning steps
@@ -146,7 +147,7 @@ def test_replanning_time(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWra
             # Check if number of steps until termination match the replanning interval
             print(done, (i + 1), episode_steps)
             assert (i + 1) % episode_steps == 0
-            env.reset()
+            env.reset(seed=SEED)
 
         assert replanning_schedule(None, None, None, None, length)
 
@@ -171,7 +172,7 @@ def test_max_planning_times(mp_type: str, max_planning_times: int, sub_segment_s
                             {'basis_generator_type': basis_generator_type,
                              },
                             seed=SEED)
-    _ = env.reset()
+    _ = env.reset(seed=SEED)
     done = False
     planning_times = 0
     while not done:
@@ -203,7 +204,7 @@ def test_replanning_with_learn_tau(mp_type: str, max_planning_times: int, sub_se
                             {'basis_generator_type': basis_generator_type,
                              },
                             seed=SEED)
-    _ = env.reset()
+    _ = env.reset(seed=SEED)
     done = False
     planning_times = 0
     while not done:
@@ -236,7 +237,7 @@ def test_replanning_with_learn_delay(mp_type: str, max_planning_times: int, sub_
                             {'basis_generator_type': basis_generator_type,
                              },
                             seed=SEED)
-    _ = env.reset()
+    _ = env.reset(seed=SEED)
     done = False
     planning_times = 0
     while not done:
@@ -291,7 +292,7 @@ def test_replanning_with_learn_delay_and_tau(mp_type: str, max_planning_times: i
                             {'basis_generator_type': basis_generator_type,
                              },
                             seed=SEED)
-    _ = env.reset()
+    _ = env.reset(seed=SEED)
     done = False
     planning_times = 0
     while not done:
@@ -340,7 +341,7 @@ def test_replanning_schedule(mp_type: str, max_planning_times: int, sub_segment_
                             {'basis_generator_type': basis_generator_type,
                              },
                             seed=SEED)
-    _ = env.reset()
+    _ = env.reset(seed=SEED)
     for i in range(max_planning_times):
         action = env.action_space.sample()
         _obs, _reward, terminated, truncated, _info = env.step(action)

test/utils.py

@@ -30,7 +30,7 @@ def run_env(env_id: str, iterations: int = None, seed: int = 0, wrappers: List[T
     actions = []
     terminations = []
     truncations = []
-    obs, _ = env.reset()
+    obs, _ = env.reset(seed=seed)
     verify_observations(obs, env.observation_space, "reset()")
 
     iterations = iterations or (env.spec.max_episode_steps or 1)