44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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Author: John
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Email: johnjim0816@gmail.com
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Date: 2021-03-23 15:30:46
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LastEditor: John
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LastEditTime: 2021-09-26 22:00:07
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Discription:
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Environment:
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'''
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import numpy as np
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class PPOMemory:
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def __init__(self, batch_size):
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self.states = []
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self.probs = []
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self.vals = []
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self.actions = []
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self.rewards = []
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self.dones = []
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self.batch_size = batch_size
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def sample(self):
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batch_step = np.arange(0, len(self.states), self.batch_size)
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indices = np.arange(len(self.states), dtype=np.int64)
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np.random.shuffle(indices)
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batches = [indices[i:i+self.batch_size] for i in batch_step]
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return np.array(self.states),np.array(self.actions),np.array(self.probs),\
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np.array(self.vals),np.array(self.rewards),np.array(self.dones),batches
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def push(self, state, action, probs, vals, reward, done):
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self.states.append(state)
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self.actions.append(action)
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self.probs.append(probs)
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self.vals.append(vals)
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self.rewards.append(reward)
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self.dones.append(done)
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def clear(self):
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self.states = []
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self.probs = []
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self.actions = []
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self.rewards = []
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self.dones = []
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self.vals = [] |