| import os |
| _OBS = os.environ.get("RL_OBS_DIM"); _ACT = os.environ.get("RL_N_ACT") |
| if _OBS and _ACT: |
| _OBS, _ACT = int(_OBS), int(_ACT) |
| import numpy as np, gymnasium as gym |
|
|
| class _Unify(gym.Wrapper): |
| def __init__(self, env): |
| super().__init__(env) |
| self.n_valid = int(env.action_space.n) |
| sp = env.observation_space |
| self._pad = _OBS - int(sp.shape[0]) |
| assert self._pad >= 0, f"RL_OBS_DIM={_OBS} is smaller than the observation dimension {sp.shape[0]}" |
| z = np.zeros(self._pad, dtype=np.float32) |
| self.observation_space = gym.spaces.Box( |
| low=np.concatenate([sp.low.astype(np.float32), z - np.inf if False else z]), |
| high=np.concatenate([sp.high.astype(np.float32), z]), |
| dtype=np.float32) |
| self.action_space = gym.spaces.Discrete(_ACT) |
| def _o(self, o): |
| return np.concatenate([np.asarray(o, dtype=np.float32), |
| np.zeros(self._pad, dtype=np.float32)]) |
| def reset(self, **kw): |
| o, i = self.env.reset(**kw); return self._o(o), i |
| def step(self, a): |
| a = int(a) |
| if a >= self.n_valid: |
| a = self.n_valid - 1 |
| _Unify.n_clipped += 1 |
| o, r, te, tr, i = self.env.step(a); return self._o(o), r, te, tr, i |
| _Unify.n_clipped = 0 |
|
|
| _orig = gym.make |
| def _make(env_id, *a, **kw): |
| return _Unify(_orig(env_id, *a, **kw)) |
| gym.make = _make |
|
|
