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