【強化學習筆記】4.2 無模型的強化學習方法-蒙特卡羅演算法程式設計實現
阿新 • • 發佈：2019-02-02
本文給出基於蒙特卡洛的強化學習方法（隨機策略計算狀態值函式）和基於蒙特卡洛的強化學習方法（ε−greedy策略計算狀態行為值函式）兩種方法的程式設計實現。
問題模型是迷宮問題。
針對一個迷宮問題，設計基於蒙特卡洛的強化學習方法。
迷宮圖示見下圖，其中紅色部分為障礙物，綠色部分為出口：
基於蒙特卡洛的強化學習方法（隨機策略計算狀態值函式）
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#import gym
import random
#import numpy as np
class GriDMdp:
def __init__ 基於蒙特卡洛的強化學習方法（ε−greedy策略計算狀態行為值函式）
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#import gym
import random
import numpy as np
class GriDMdp:
def __init__(s):
s.gamma = 0.9
s.epsilon = 0.1
s.states = range(1,26) #״̬¿Õ¼ä
s.actions = ['n', 'e', 's', 'w'] #¶¯×÷¿Õ¼ä
s.terminate_states = {15:1.0, 4:-1.0, 9:-1.0, \
11:-1.0, 12:-1.0, 23:-1.0, 24:-1.0, 25:-1.0} #½áÊø״̬
s.trans = {} #״̬ϵĶ¯×÷¿Õ¼ä
for state in s.states:
if not state in s.terminate_states:
s.trans[state] = {}
s.trans[1]['e'] = 2
s.trans[1]['s'] = 6
s.trans[2]['e'] = 3
s.trans[2]['w'] = 1
s.trans[2]['s'] = 7
s.trans[3]['e'] = 4
s.trans[3]['w'] = 2
s.trans[3]['s'] = 8
s.trans[5]['w'] = 4
s.trans[5]['s'] = 10
s.trans[6]['e'] = 7
s.trans[6]['s'] = 11
s.trans[6]['n'] = 1
s.trans[7]['e'] = 8
s.trans[7]['w'] = 6
s.trans[7]['s'] = 12
s.trans[7]['n'] = 2
s.trans[8]['e'] = 9
s.trans[8]['w'] = 7
s.trans[8]['s'] = 13
s.trans[8]['n'] = 3
s.trans[10]['w'] = 9
s.trans[10]['s'] = 15
s.trans[13]['e'] = 14
s.trans[13]['w'] = 12
s.trans[13]['s'] = 18
s.trans[13]['n'] = 8
s.trans[14]['e'] = 15
s.trans[14]['w'] = 13
s.trans[14]['s'] = 19
s.trans[14]['n'] = 9
s.trans[16]['e'] = 17
s.trans[16]['s'] = 21
s.trans[16]['n'] = 11
s.trans[17]['e'] = 18
s.trans[17]['w'] = 16
s.trans[17]['s'] = 22
s.trans[17]['n'] = 12
s.trans[18]['e'] = 19
s.trans[18]['w'] = 17
s.trans[18]['s'] = 23
s.trans[18]['n'] = 13
s.trans[19]['e'] = 20
s.trans[19]['w'] = 18
s.trans[19]['s'] = 24
s.trans[19]['n'] = 14
s.trans[20]['w'] = 19
s.trans[20]['s'] = 25
s.trans[20]['n'] = 15
s.trans[21]['e'] = 22
s.trans[21]['n'] = 16
s.trans[22]['e'] = 23
s.trans[22]['w'] = 21
s.trans[22]['n'] = 17
s.rewards = {} #½±Àø
for state in s.states:
s.rewards[state] = {}
for action in s.actions:
s.rewards[state][action] = 0
if state in s.trans and action in s.trans[state]:
next_state = s.trans[state][action]
if next_state in s.terminate_states:
s.rewards[state][action] = s.terminate_states[next_state]
s.pi = {} #²ßÂÔ
for state in s.trans:
s.pi[state] = random.choice(s.trans[state].keys())
s.last_pi = s.pi.copy()
s.v = {} #״ֵ̬º¯Êý
for state in s.states:
s.v[state] = 0.0
def get_random_action(s, state):
s.pi[state] = random.choice(s.trans[state].keys())
return s.pi[state]
def transform(s, state, action):
next_state = state
state_reward = 0
is_terminate = True
return_info = {}
if state in s.terminate_states:
return next_state, state_reward, is_terminate, return_info
if state in s.trans:
if action in s.trans[state]:
next_state = s.trans[state][action]
if state in s.rewards:
if action in s.rewards[state]:
state_reward = s.rewards[state][action]
if not next_state in s.terminate_states:
is_terminate = False
return next_state, state_reward, is_terminate, return_info
def print_states(s):
for state in s.states:
if state in s.terminate_states:
print "*",
else:
print round(s.v[state], 2),
if state % 5 == 0:
print "|"
def epsilon_greey(state_action_value_dic, state, epsilon):
action_list = state_action_value_dic[state].keys()
len_action = len(action_list)
action_prob = [epsilon / float(len_action)] * len_action
max_val = float('-inf')
max_idx = -1
for idx in range(len_action):
action = action_list[idx]
state_action_value = state_action_value_dic[state][action][1]
if state_action_value > max_val:
max_val = state_action_value
max_idx = idx
if max_idx < 0:
return np.random.choice(action_list)
else:
action_prob[max_idx] += (1 - epsilon)
epsilon_greey_action = np.random.choice(action_list, p=action_prob)
return epsilon_greey_action
def monte_carlo_epsilon_greey(grid_mdp):
'''Ëæ»úÑ¡Ôñ״̬£¬epsilon_greey²ßÂÔÑ¡Ôñ״̬ÏÂÃæµÄ¶¯×÷£¬Éú³ÉÊý¾Ý¼¯ºÏ'''
state_action_value_dic = {}
for iter_idx in range(100000):
#print "-----------------------"
one_sample_list = []
state = random.choice(grid_mdp.states)
while(state in grid_mdp.terminate_states):
state = random.choice(grid_mdp.states)
sample_end = False
while sample_end != True:
if not state in state_action_value_dic:
state_action_value_dic[state] = {}
# choose epsilon_greey strategy
for action in grid_mdp.trans[state]:
if not action in state_action_value_dic[state]:
state_action_value_dic[state][action] = [0.0, 0.0]
action = epsilon_greey(state_action_value_dic, state, grid_mdp.epsilon)
next_state, state_reward, is_terminate, return_info = grid_mdp.transform(state, action)
one_sample_list.append((state, action, state_reward))
state = next_state
sample_end = is_terminate
#compute state_action_value
G = 0.0
#print one_sample_list
for idx in range(len(one_sample_list)-1, -1, -1):
one_sample = one_sample_list[idx]
state = one_sample[0]
action = one_sample[1]
state_reward = one_sample[2]
if not state in state_action_value_dic:
state_action_value_dic[state] = {}
if not action in state_action_value_dic[state]:
state_action_value_dic[state][action] =[0.0, 0.0]
G = state_reward + grid_mdp.gamma * G
state_action_value_dic[state][action][0] += 1
state_action_value_dic[state][action][1] += ((G - state_action_value_dic[state][action][1]) / state_action_value_dic[state][action][0])
if iter_idx % 10000 == 0:
print "-"*18
for state in sorted(state_action_value_dic.keys()):
for action in sorted(state_action_value_dic[state]):
print state,action,state_action_value_dic[state][action]
grid_mdp = GriDMdp()
monte_carlo_epsilon_greey(grid_mdp)