Re-add functional api to experimental

gymnasium/__init__.py

@@ -20,7 +20,7 @@
     VectorizeMode,
     register_envs,
 )
-from gymnasium import spaces, utils, vector, wrappers, error, logger, functional
+from gymnasium import spaces, utils, vector, wrappers, error, logger, experimental
 
 # Initializing pygame initializes audio connections through SDL. SDL uses alsa by default on all Linux systems
 # SDL connecting to alsa frequently create these giant lists of warnings every time you import an environment using
@@ -64,7 +64,7 @@
     "wrappers",
     "error",
     "logger",
-    "functional",
+    "experimental",
 ]
 __version__ = "1.0.0"
 

gymnasium/envs/functional_jax_env.py

@@ -10,7 +10,7 @@
 
 import gymnasium as gym
 from gymnasium.envs.registration import EnvSpec
-from gymnasium.functional import ActType, FuncEnv, StateType
+from gymnasium.experimental.functional import ActType, FuncEnv, StateType
 from gymnasium.utils import seeding
 from gymnasium.vector.utils import batch_space
 

gymnasium/envs/phys2d/cartpole.py

@@ -13,7 +13,7 @@
 import gymnasium as gym
 from gymnasium.envs.functional_jax_env import FunctionalJaxEnv, FunctionalJaxVectorEnv
 from gymnasium.error import DependencyNotInstalled
-from gymnasium.functional import ActType, FuncEnv, StateType
+from gymnasium.experimental.functional import ActType, FuncEnv, StateType
 from gymnasium.utils import EzPickle
 
 

gymnasium/envs/phys2d/pendulum.py

@@ -14,7 +14,7 @@
 import gymnasium as gym
 from gymnasium.envs.functional_jax_env import FunctionalJaxEnv, FunctionalJaxVectorEnv
 from gymnasium.error import DependencyNotInstalled
-from gymnasium.functional import ActType, FuncEnv, StateType
+from gymnasium.experimental.functional import ActType, FuncEnv, StateType
 from gymnasium.utils import EzPickle
 
 

gymnasium/envs/tabular/blackjack.py

@@ -14,7 +14,7 @@
 from gymnasium import spaces
 from gymnasium.envs.functional_jax_env import FunctionalJaxEnv
 from gymnasium.error import DependencyNotInstalled
-from gymnasium.functional import ActType, FuncEnv, StateType
+from gymnasium.experimental.functional import ActType, FuncEnv, StateType
 from gymnasium.utils import EzPickle, seeding
 from gymnasium.wrappers import HumanRendering
 

gymnasium/envs/tabular/cliffwalking.py

@@ -13,7 +13,7 @@
 from gymnasium import spaces
 from gymnasium.envs.functional_jax_env import FunctionalJaxEnv
 from gymnasium.error import DependencyNotInstalled
-from gymnasium.functional import ActType, FuncEnv, StateType
+from gymnasium.experimental.functional import ActType, FuncEnv, StateType
 from gymnasium.utils import EzPickle
 from gymnasium.wrappers import HumanRendering
 

gymnasium/experimental/__init__.py

@@ -0,0 +1 @@
+"""Experimental module."""

tests/envs/functional/test_core.py

@@ -2,7 +2,7 @@
 
 import numpy as np
 
-from gymnasium.functional import FuncEnv
+from gymnasium.experimental.functional import FuncEnv
 
 
 class BasicTestEnv(FuncEnv):

tests/functional/test_functional.py

@@ -6,7 +6,7 @@
 
 import numpy as np
 
-from gymnasium.functional import FuncEnv
+from gymnasium.experimental.functional import FuncEnv
 
 
 class GenericTestFuncEnv(FuncEnv):
@@ -16,25 +16,32 @@ def __init__(self, options: dict[str, Any] | None = None):
         """Constructor that allows generic options to be set on the environment."""
         super().__init__(options)
 
-    def initial(self, rng: Any) -> np.ndarray:
+    def initial(self, rng: Any, params=None) -> np.ndarray:
         """Testing initial function."""
         return np.array([0, 0], dtype=np.float32)
 
-    def observation(self, state: np.ndarray, rng: Any) -> np.ndarray:
+    def observation(self, state: np.ndarray, rng: Any, params=None) -> np.ndarray:
         """Testing observation function."""
         return state
 
-    def transition(self, state: np.ndarray, action: int, rng: None) -> np.ndarray:
+    def transition(
+        self, state: np.ndarray, action: int, rng: None, params=None
+    ) -> np.ndarray:
         """Testing transition function."""
         return state + np.array([0, action], dtype=np.float32)
 
     def reward(
-        self, state: np.ndarray, action: int, next_state: np.ndarray, rng: Any
+        self,
+        state: np.ndarray,
+        action: int,
+        next_state: np.ndarray,
+        rng: Any,
+        params=None,
     ) -> float:
         """Testing reward function."""
         return 1.0 if next_state[1] > 0 else 0.0
 
-    def terminal(self, state: np.ndarray, rng: Any) -> bool:
+    def terminal(self, state: np.ndarray, rng: Any, params=None) -> bool:
         """Testing terminal function."""
         return state[1] > 0