65 lines
2.5 KiB
Python
65 lines
2.5 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: 2021-03-12 16:14:34
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LastEditor: John
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LastEditTime: 2021-05-05 16:58:39
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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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from collections import defaultdict
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import torch
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import dill
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class FisrtVisitMC:
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''' On-Policy First-Visit MC Control
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'''
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def __init__(self,n_actions,cfg):
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self.n_actions = n_actions
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self.epsilon = cfg.epsilon
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self.gamma = cfg.gamma
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self.Q_table = defaultdict(lambda: np.zeros(n_actions))
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self.returns_sum = defaultdict(float) # sum of returns
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self.returns_count = defaultdict(float)
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def choose_action(self,state):
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''' e-greed policy '''
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if state in self.Q_table.keys():
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best_action = np.argmax(self.Q_table[state])
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action_probs = np.ones(self.n_actions, dtype=float) * self.epsilon / self.n_actions
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action_probs[best_action] += (1.0 - self.epsilon)
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action = np.random.choice(np.arange(len(action_probs)), p=action_probs)
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else:
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action = np.random.randint(0,self.n_actions)
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return action
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def update(self,one_ep_transition):
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# Find all (state, action) pairs we've visited in this one_ep_transition
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# We convert each state to a tuple so that we can use it as a dict key
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sa_in_episode = set([(tuple(x[0]), x[1]) for x in one_ep_transition])
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for state, action in sa_in_episode:
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sa_pair = (state, action)
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# Find the first occurence of the (state, action) pair in the one_ep_transition
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first_occurence_idx = next(i for i,x in enumerate(one_ep_transition)
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if x[0] == state and x[1] == action)
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# Sum up all rewards since the first occurance
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G = sum([x[2]*(self.gamma**i) for i,x in enumerate(one_ep_transition[first_occurence_idx:])])
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# Calculate average return for this state over all sampled episodes
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self.returns_sum[sa_pair] += G
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self.returns_count[sa_pair] += 1.0
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self.Q_table[state][action] = self.returns_sum[sa_pair] / self.returns_count[sa_pair]
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def save(self,path):
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'''把 Q表格 的数据保存到文件中
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'''
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torch.save(
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obj=self.Q_table,
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f=path+"Q_table",
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pickle_module=dill
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)
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def load(self, path):
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'''从文件中读取数据到 Q表格
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'''
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self.Q_table =torch.load(f=path+"Q_table",pickle_module=dill) |