基于python的强化学习算法Q-learning设计与实现

""" 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()