Move dev_wrappers and functional to experimental (#159)

tests/experimental/functional/test_core.py

@@ -0,0 +1,56 @@
+from typing import Any, Dict, Optional
+
+import numpy as np
+
+from gymnasium.experimental import FuncEnv
+
+
+class GenericTestFuncEnv(FuncEnv):
+    def __init__(self, options: Optional[Dict[str, Any]] = None):
+        super().__init__(options)
+
+    def initial(self, rng: Any) -> np.ndarray:
+        return np.array([0, 0], dtype=np.float32)
+
+    def observation(self, state: np.ndarray) -> np.ndarray:
+        return state
+
+    def transition(self, state: np.ndarray, action: int, rng: None) -> np.ndarray:
+        return state + np.array([0, action], dtype=np.float32)
+
+    def reward(self, state: np.ndarray, action: int, next_state: np.ndarray) -> float:
+        return 1.0 if next_state[1] > 0 else 0.0
+
+    def terminal(self, state: np.ndarray) -> bool:
+        return state[1] > 0
+
+
+def test_api():
+    env = GenericTestFuncEnv()
+    state = env.initial(None)
+    obs = env.observation(state)
+    assert state.shape == (2,)
+    assert state.dtype == np.float32
+    assert obs.shape == (2,)
+    assert obs.dtype == np.float32
+    assert np.allclose(obs, state)
+
+    actions = [-1, -2, -5, 3, 5, 2]
+    for i, action in enumerate(actions):
+        next_state = env.transition(state, action, None)
+        assert next_state.shape == (2,)
+        assert next_state.dtype == np.float32
+        assert np.allclose(next_state, state + np.array([0, action]))
+
+        observation = env.observation(next_state)
+        assert observation.shape == (2,)
+        assert observation.dtype == np.float32
+        assert np.allclose(observation, next_state)
+
+        reward = env.reward(state, action, next_state)
+        assert reward == (1.0 if next_state[1] > 0 else 0.0)
+
+        terminal = env.terminal(next_state)
+        assert terminal == (i == 5)  # terminal state is in the final action
+
+        state = next_state