【强化学习】python 实现 q-learning 迷宫通用模板
本文作者：hhh5460
本文地址：https://www.cnblogs.com/hhh5460/p/10145797.html
0.说明
这里提供了二维迷宫问题的一个比较通用的模板，拿到后需要修改的地方非常少。
对于任意的二维迷宫的 class Agent，只需修改三个地方：MAZE_R， MAZE_R， rewards，其他的不要动！如下所示：
class Agent(object): '''个体类''' MAZE_R = 6 # 迷宫行数 MAZE_C = 6 # 迷宫列数 def __init__(self, alpha=0.1, gamma=0.9): '''初始化''' # ... ... self.rewards = [0,-10,0, 0, 0, 0, 0,-10,0, 0,-10, 0, 0,-10,0,-10, 0, 0, 0,-10,0,-10, 0, 0, 0,-10,0,-10, 1, 0, 0, 0,0,-10, 0,10,] # 奖励集。出口奖励10，陷阱奖励-10，元宝奖励1 # ... ...
1.完整代码
import pandas as pd import random import time import pickle import pathlib import os import tkinter as tk ''' 6*6 的迷宫： ------------------------------------------- | 入口 | 陷阱 | | | | | ------------------------------------------- | | 陷阱 | | | 陷阱 | | ------------------------------------------- | | 陷阱 | | 陷阱 | | | ------------------------------------------- | | 陷阱 | | 陷阱 | | | ------------------------------------------- | | 陷阱 | | 陷阱 | 元宝 | | ------------------------------------------- | | | | 陷阱 | | 出口 | ------------------------------------------- 作者：hhh5460 时间：20181219 地点：Tai Zi Miao ''' class Maze(tk.Tk): '''环境类（GUI）''' UNIT = 40 # pixels MAZE_R = 6 # grid row MAZE_C = 6 # grid column def __init__(self): '''初始化''' super().__init__() self.title('迷宫') h = self.MAZE_R * self.UNIT w = self.MAZE_C * self.UNIT self.geometry('{0}x{1}'.format(h, w)) #窗口大小 self.canvas = tk.Canvas(self, bg='white', height=h, width=w) # 画网格 for c in range(1, self.MAZE_C): self.canvas.create_line(c * self.UNIT, 0, c * self.UNIT, h) for r in range(1, self.MAZE_R): self.canvas.create_line(0, r * self.UNIT, w, r * self.UNIT) # 画陷阱 self._draw_rect(1, 0, 'black') # 在1列、0行处，下同 self._draw_rect(1, 1, 'black') self._draw_rect(1, 2, 'black') self._draw_rect(1, 3, 'black') self._draw_rect(1, 4, 'black') self._draw_rect(3, 2, 'black') self._draw_rect(3, 3, 'black') self._draw_rect(3, 4, 'black') self._draw_rect(3, 5, 'black') self._draw_rect(4, 1, 'black') # 画奖励 self._draw_rect(4, 4, 'yellow') # 画玩家(保存!!) self.rect = self._draw_rect(0, 0, 'red') self.canvas.pack() # 显示画作！ def _draw_rect(self, x, y, color): '''画矩形， x,y表示横,竖第几个格子''' padding = 5 # 内边距5px，参见CSS coor = [self.UNIT * x + padding, self.UNIT * y + padding, self.UNIT * (x+1) - padding, self.UNIT * (y+1) - padding] return self.canvas.create_rectangle(*coor, fill = color) def move_agent_to(self, state, step_time=0.01): '''移动玩家到新位置，根据传入的状态''' coor_old = self.canvas.coords(self.rect) # 形如[5.0, 5.0, 35.0, 35.0]（第一个格子左上、右下坐标） x, y = state % 6, state // 6 #横竖第几个格子 padding = 5 # 内边距5px，参见CSS coor_new = [self.UNIT * x + padding, self.UNIT * y + padding, self.UNIT * (x+1) - padding, self.UNIT * (y+1) - padding] dx_pixels, dy_pixels = coor_new[0] - coor_old[0], coor_new[1] - coor_old[1] # 左上角顶点坐标之差 self.canvas.move(self.rect, dx_pixels, dy_pixels) self.update() # tkinter内置的update! time.sleep(step_time) class Agent(object): '''个体类''' MAZE_R = 6 # 迷宫行数 MAZE_C = 6 # 迷宫列数 def __init__(self, alpha=0.1, gamma=0.9): '''初始化''' self.states = range(self.MAZE_R * self.MAZE_C) # 状态集。0~35 共36个状态 self.actions = list('udlr') # 动作集。上下左右 4个动作 ↑↓←→ ←↑→↓↖↗↘↙ self.rewards = [0,-10,0, 0, 0, 0, 0,-10,0, 0,-10, 0, 0,-10,0,-10, 0, 0, 0,-10,0,-10, 0, 0, 0,-10,0,-10, 1, 0, 0, 0,0,-10, 0,10,] # 奖励集。出口奖励10，陷阱奖励-10，元宝奖励5 #self.hell_states = [1,7,13,19,25,15,31,37,43,10] # 陷阱位置 self.alpha = alpha self.gamma = gamma self.q_table = pd.DataFrame(data=[[0 for _ in self.actions] for _ in self.states], index=self.states, columns=self.actions) def save_policy(self): '''保存Q table''' with open('q_table.pickle', 'wb') as f: pickle.dump(self.q_table, f, pickle.HIGHEST_PROTOCOL) def load_policy(self): '''导入Q table''' with open('q_table.pickle', 'rb') as f: self.q_table = pickle.load(f) def choose_action(self, state, epsilon=0.8): '''选择相应的动作。根据当前状态，随机或贪婪，按照参数epsilon''' #if (random.uniform(0,1) > epsilon) or ((self.q_table.ix[state] == 0).all()): # 探索 if random.uniform(0,1) > epsilon: # 探索 action = random.choice(self.get_valid_actions(state)) else: #action = self.q_table.ix[state].idxmax() # 利用 当有多个最大值时，会锁死第一个！ #action = self.q_table.ix[state].filter(items=self.get_valid_actions(state)).idxmax() # 重大改进！然鹅与上面一样 s = self.q_table.ix[state].filter(items=self.get_valid_actions(state)) action = random.choice(s[s==s.max()].index) # 从可能有多个的最大值里面随机选择一个！ return action def get_q_values(self, state): '''取给定状态state的所有Q value''' q_values = self.q_table.ix[state, self.get_valid_actions(state)] return q_values def update_q_value(self, state, action, next_state_reward, next_state_q_values): '''更新Q value，根据贝尔曼方程''' self.q_table.ix[state, action] += self.alpha * (next_state_reward + self.gamma * next_state_q_values.max() - self.q_table.ix[state, action]) def get_valid_actions(self, state): '''取当前状态下所有的合法动作''' valid_actions = set(self.actions) if state // self.MAZE_C == 0: # 首行，则 不能向上 valid_actions -= set(['u']) elif state // self.MAZE_C == self.MAZE_R - 1: # 末行，则 不能向下 valid_actions -= set(['d']) if state % self.MAZE_C == 0: # 首列，则 不能向左 valid_actions -= set(['l']) elif state % self.MAZE_C == self.MAZE_C - 1: # 末列，则 不能向右 valid_actions -= set(['r']) return list(valid_actions) def get_next_state(self, state, action): '''对状态执行动作后，得到下一状态''' #u,d,l,r,n = -6,+6,-1,+1,0 if action == 'u' and state // self.MAZE_C != 0: # 除首行外，向上-MAZE_C next_state = state - self.MAZE_C elif action == 'd' and state // self.MAZE_C != self.MAZE_R - 1: # 除末行外，向下+MAZE_C next_state = state + self.MAZE_C elif action == 'l' and state % self.MAZE_C != 0: # 除首列外，向左-1 next_state = state - 1 elif action == 'r' and state % self.MAZE_C != self.MAZE_C - 1: # 除末列外，向右+1 next_state = state + 1 else: next_state = state return next_state def learn(self, env=None, episode=1000, epsilon=0.8): '''q-learning算法''' print('Agent is learning...') for i in range(episode): current_state = self.states[0] if env is not None: # 若提供了环境，则重置之！ env.move_agent_to(current_state) while current_state != self.states[-1]: current_action = self.choose_action(current_state, epsilon) # 按一定概率，随机或贪婪地选择 next_state = self.get_next_state(current_state, current_action) next_state_reward = self.rewards[next_state] next_state_q_values = self.get_q_values(next_state) self.update_q_value(current_state, current_action, next_state_reward, next_state_q_values) current_state = next_state #if next_state not in self.hell_states: # 非陷阱，则往前；否则待在原位 # current_state = next_state if env is not None: # 若提供了环境，则更新之！ env.move_agent_to(current_state) print(i) print('\nok') def test(self): '''测试agent是否已具有智能''' count = 0 current_state = self.states[0] while current_state != self.states[-1]: current_action = self.choose_action(current_state, 1.) # 1., 100%贪婪 next_state = self.get_next_state(current_state, current_action) current_state = next_state count += 1 if count > self.MAZE_R * self.MAZE_C: # 没有在36步之内走出迷宫，则 return False # 无智能 return True # 有智能 def play(self, env=None, step_time=0.5): '''玩游戏，使用策略''' assert env != None, 'Env must be not None!' if not self.test(): # 若尚无智能，则 if pathlib.Path("q_table.pickle").exists(): self.load_policy() else: print("I need to learn before playing this game.") self.learn(env, episode=1000, epsilon=0.5) self.save_policy() print('Agent is playing...') current_state = self.states[0] env.move_agent_to(current_state, step_time) while current_state != self.states[-1]: current_action = self.choose_action(current_state, 1.) # 1., 100%贪婪 next_state = self.get_next_state(current_state, current_action) current_state = next_state env.move_agent_to(current_state, step_time) print('\nCongratulations, Agent got it!') if __name__ == '__main__': env = Maze() # 环境 agent = Agent() # 个体（智能体） agent.learn(env, episode=1000, epsilon=0.6) # 先学习 #agent.save_policy() #agent.load_policy() agent.play(env) # 再玩耍 #env.after(0, agent.learn, env, 1000, 0.8) # 先学 #env.after(0, agent.save_policy) # 保存所学 #env.after(0, agent.load_policy) # 导入所学 #env.after(0, agent.play, env) # 再玩 env.mainloop()
Just enjoy it!
