求Python Qlearning 迷宫机器人代码

[sunqian0715]

8*8的迷宫，利用Qlearnin算法走出迷宫

[qq_34664921]

你好，请问你有这个程序了吗。Q learning实现迷宫搜索的

[khk_abc]

""" Reinforcement learning maze example. Red rectangle: explorer. Black rectangles: hells [reward = -1]. Yellow bin circle: paradise [reward = +1]. All other states: ground [reward = 0]. This script is the environment part of this example. The RL is in RL_brain.py. View more on my tutorial page: https://morvanzhou.github.io/tutorials/ """ import numpy as np import time import sys if sys.version_info.major == 2: import Tkinter as tk else: import tkinter as tk UNIT = 40 # pixels MAZE_H = 4 # grid height MAZE_W = 4 # grid width class Maze(tk.Tk, object): def __init__(self): super(Maze, self).__init__() self.action_space = ['u', 'd', 'l', 'r'] self.n_actions = len(self.action_space) self.title('maze') self.geometry('{0}x{1}'.format(MAZE_H * UNIT, MAZE_H * UNIT)) self._build_maze() def _build_maze(self): self.canvas = tk.Canvas(self, bg='white', height=MAZE_H * UNIT, width=MAZE_W * UNIT) # create grids for c in range(0, MAZE_W * UNIT, UNIT): x0, y0, x1, y1 = c, 0, c, MAZE_H * UNIT self.canvas.create_line(x0, y0, x1, y1) for r in range(0, MAZE_H * UNIT, UNIT): x0, y0, x1, y1 = 0, r, MAZE_W * UNIT, r self.canvas.create_line(x0, y0, x1, y1) # create origin origin = np.array([20, 20]) # hell hell1_center = origin + np.array([UNIT * 2, UNIT]) self.hell1 = self.canvas.create_rectangle( hell1_center[0] - 15, hell1_center[1] - 15, hell1_center[0] + 15, hell1_center[1] + 15, fill='black') # hell hell2_center = origin + np.array([UNIT, UNIT * 2]) self.hell2 = self.canvas.create_rectangle( hell2_center[0] - 15, hell2_center[1] - 15, hell2_center[0] + 15, hell2_center[1] + 15, fill='black') # create oval oval_center = origin + UNIT * 2 self.oval = self.canvas.create_oval( oval_center[0] - 15, oval_center[1] - 15, oval_center[0] + 15, oval_center[1] + 15, fill='yellow') # create red rect self.rect = self.canvas.create_rectangle( origin[0] - 15, origin[1] - 15, origin[0] + 15, origin[1] + 15, fill='red') # pack all self.canvas.pack() def reset(self): self.update() time.sleep(0.5) self.canvas.delete(self.rect) origin = np.array([20, 20]) self.rect = self.canvas.create_rectangle( origin[0] - 15, origin[1] - 15, origin[0] + 15, origin[1] + 15, fill='red') # return observation return self.canvas.coords(self.rect) def step(self, action): s = self.canvas.coords(self.rect) base_action = np.array([0, 0]) if action == 0: # up if s[1] > UNIT: base_action[1] -= UNIT elif action == 1: # down if s[1] < (MAZE_H - 1) * UNIT: base_action[1] += UNIT elif action == 2: # right if s[0] < (MAZE_W - 1) * UNIT: base_action[0] += UNIT elif action == 3: # left if s[0] > UNIT: base_action[0] -= UNIT self.canvas.move(self.rect, base_action[0], base_action[1]) # move agent s_ = self.canvas.coords(self.rect) # next state # reward function if s_ == self.canvas.coords(self.oval): reward = 1 done = True s_ = 'terminal' elif s_ in [self.canvas.coords(self.hell1), self.canvas.coords(self.hell2)]: reward = -1 done = True s_ = 'terminal' else: reward = 0 done = False return s_, reward, done def render(self): time.sleep(0.1) self.update() def update(): for t in range(10): s = env.reset() while True: env.render() a = 1 s, r, done = env.step(a) if done: break if __name__ == '__main__': env = Maze() env.after(100, update) env.mainloop() """ This part of code is the Q learning brain, which is a brain of the agent. All decisions are made in here. View more on my tutorial page: https://morvanzhou.github.io/tutorials/ """ import numpy as np import pandas as pd class QLearningTable: def __init__(self, actions, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9): self.actions = actions # a list self.lr = learning_rate self.gamma = reward_decay self.epsilon = e_greedy self.q_table = pd.DataFrame(columns=self.actions, dtype=np.float64) def choose_action(self, observation): self.check_state_exist(observation) # action selection if np.random.uniform() < self.epsilon: # choose best action state_action = self.q_table.loc[observation, :] # some actions may have the same value, randomly choose on in these actions action = np.random.choice(state_action[state_action == np.max(state_action)].index) else: # choose random action action = np.random.choice(self.actions) return action def learn(self, s, a, r, s_): self.check_state_exist(s_) q_predict = self.q_table.loc[s, a] if s_ != 'terminal': q_target = r + self.gamma * self.q_table.loc[s_, :].max() # next state is not terminal else: q_target = r # next state is terminal self.q_table.loc[s, a] += self.lr * (q_target - q_predict) # update def check_state_exist(self, state): if state not in self.q_table.index: # append new state to q table self.q_table = self.q_table.append( pd.Series( [0]*len(self.actions), index=self.q_table.columns, name=state, ) ) """ Reinforcement learning maze example. Red rectangle: explorer. Black rectangles: hells [reward = -1]. Yellow bin circle: paradise [reward = +1]. All other states: ground [reward = 0]. This script is the main part which controls the update method of this example. The RL is in RL_brain.py. View more on my tutorial page: https://morvanzhou.github.io/tutorials/ """ from maze_env import Maze from RL_brain import QLearningTable def update(): for episode in range(100): # initial observation observation = env.reset() while True: # fresh env env.render() # RL choose action based on observation action = RL.choose_action(str(observation)) # RL take action and get next observation and reward observation_, reward, done = env.step(action) # RL learn from this transition RL.learn(str(observation), action, reward, str(observation_)) # swap observation observation = observation_ # break while loop when end of this episode if done: break # end of game print('game over') env.destroy() if __name__ == "__main__": env = Maze() RL = QLearningTable(actions=list(range(env.n_actions))) env.after(100, update) env.mainloop()