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import numpy as np |
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class RandomAgent: |
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def __init__(self, obs_space, act_space): |
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self.obs_space = obs_space |
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self.act_space = act_space |
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def init_policy(self, batch_size): |
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return () |
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def init_train(self, batch_size): |
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return () |
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def init_report(self, batch_size): |
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return () |
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def policy(self, carry, obs, mode='train'): |
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batch_size = len(obs['is_first']) |
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act = { |
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k: np.stack([v.sample() for _ in range(batch_size)]) |
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for k, v in self.act_space.items() if k != 'reset'} |
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return carry, act, {} |
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def train(self, carry, data): |
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return carry, {}, {} |
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def report(self, carry, data): |
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return carry, {} |
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def stream(self, st): |
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return st |
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def save(self): |
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return None |
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def load(self, data=None): |
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pass |
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