50 lines
1.9 KiB
Python
50 lines
1.9 KiB
Python
from baselines.common.vec_env import VecEnvWrapper
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from baselines.common.running_mean_std import RunningMeanStd
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import numpy as np
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class VecNormalize(VecEnvWrapper):
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"""
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Vectorized environment base class
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"""
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def __init__(self, venv, ob=True, ret=True, clipob=10., cliprew=10., gamma=0.99, epsilon=1e-8):
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VecEnvWrapper.__init__(self, venv)
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self.ob_rms = RunningMeanStd(shape=self.observation_space.shape) if ob else None
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self.ret_rms = RunningMeanStd(shape=()) if ret else None
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#self.ob_rms = TfRunningMeanStd(shape=self.observation_space.shape, scope='observation_running_mean_std') if ob else None
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#self.ret_rms = TfRunningMeanStd(shape=(), scope='return_running_mean_std') if ret else None
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self.clipob = clipob
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self.cliprew = cliprew
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self.ret = np.zeros(self.num_envs)
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self.gamma = gamma
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self.epsilon = epsilon
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def step_wait(self):
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"""
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Apply sequence of actions to sequence of environments
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actions -> (observations, rewards, news)
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where 'news' is a boolean vector indicating whether each element is new.
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"""
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obs, rews, news, infos = self.venv.step_wait()
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self.ret = self.ret * self.gamma + rews
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obs = self._obfilt(obs)
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if self.ret_rms:
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self.ret_rms.update(self.ret)
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rews = np.clip(rews / np.sqrt(self.ret_rms.var + self.epsilon), -self.cliprew, self.cliprew)
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return obs, rews, news, infos
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def _obfilt(self, obs):
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if self.ob_rms:
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self.ob_rms.update(obs)
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obs = np.clip((obs - self.ob_rms.mean) / np.sqrt(self.ob_rms.var + self.epsilon), -self.clipob, self.clipob)
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return obs
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else:
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return obs
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def reset(self):
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"""
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Reset all environments
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"""
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obs = self.venv.reset()
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return self._obfilt(obs)
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