mirror of
https://github.com/wassname/ray.git
synced 2026-07-09 12:18:44 +08:00
Robert Nishihara
3ebfd850e1
* Test examples for pep8 compliance. * Make rl_pong example pep8 compliant. * Make policy gradient example pep8 compliant. * Make lbfgs example pep8 compliant. * Make hyperopt example pep8 compliant. * Make a3c example pep8 compliant. * Make evolution strategies example pep8 compliant. * Make resnet example pep8 compliant. * Fix.
66 lines
1.9 KiB
Python
66 lines
1.9 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import gym
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import numpy as np
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class AtariPixelPreprocessor(object):
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def __init__(self):
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self.shape = (80, 80, 3)
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def __call__(self, observation):
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"Convert images from (210, 160, 3) to (3, 80, 80) by downsampling."
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return (observation[25:-25:2, ::2, :][None] - 128) / 128
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class AtariRamPreprocessor(object):
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def __init__(self):
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self.shape = (128,)
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def __call__(self, observation):
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return (observation - 128) / 128
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class NoPreprocessor(object):
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def __init__(self):
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self.shape = None
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def __call__(self, observation):
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return observation
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class BatchedEnv(object):
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"""This holds multiple gym enviroments and performs steps on all of them."""
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def __init__(self, name, batchsize, preprocessor=None):
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self.envs = [gym.make(name) for _ in range(batchsize)]
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self.observation_space = self.envs[0].observation_space
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self.action_space = self.envs[0].action_space
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self.batchsize = batchsize
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self.preprocessor = preprocessor if preprocessor else lambda obs: obs[None]
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def reset(self):
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observations = [self.preprocessor(env.reset()) for env in self.envs]
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self.shape = observations[0].shape
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self.dones = [False for _ in range(self.batchsize)]
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return np.vstack(observations)
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def step(self, actions, render=False):
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observations = []
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rewards = []
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for i, action in enumerate(actions):
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if self.dones[i]:
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observations.append(np.zeros(self.shape))
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rewards.append(0.0)
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continue
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observation, reward, done, info = self.envs[i].step(
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action if len(action) > 1 else action[0])
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if render:
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self.envs[0].render()
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observations.append(self.preprocessor(observation))
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rewards.append(reward)
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self.dones[i] = done
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return (np.vstack(observations), np.array(rewards, dtype="float32"),
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np.array(self.dones))
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