强化学习算法实例DQN代码PyTorch实现

1|0前言

实例参考MorvanZhou/Reinforcement-learning-with-tensorflow,
更改为PyTorch实现，并增加了几处优化。实现效果如下。

其中,红色方块作为探索的智能体,到达黄色圆形块reward=1,到达黑色方块区域reward=-1.

2|0代码

• 程序主循环

from dqn.maze_env import Maze
from dqn.RL_brain import DQN
import time


def run_maze():
    print("====Game Start====")
    step = 0
    max_episode = 500
    for episode in range(max_episode):
        state = env.reset()  # 重置智能体位置
        step_every_episode = 0
        epsilon = episode / max_episode  # 动态变化随机值
        while True:
            if episode < 10:
                time.sleep(0.1)
            if episode > 480:
                time.sleep(0.5)
            env.render()  # 显示新位置
            action = model.choose_action(state, epsilon)  # 根据状态选择行为
            # 环境根据行为给出下一个状态，奖励，是否结束。
            next_state, reward, terminal = env.step(action)
            model.store_transition(state, action, reward, next_state)  # 模型存储经历
            # 控制学习起始时间(先积累记忆再学习)和控制学习的频率(积累多少步经验学习一次)
            if step > 200 and step % 5 == 0:
                model.learn()
            # 进入下一步
            state = next_state
            if terminal:
                print("episode=", episode, end=",")
                print("step=", step_every_episode)
                break
            step += 1
            step_every_episode += 1
    # 游戏环境结束
    print("====Game Over====")
    env.destroy()


if __name__ == "__main__":
    env = Maze()  # 环境
    model = DQN(
        n_states=env.n_states,
        n_actions=env.n_actions
    )  # 算法模型
    run_maze()
    env.mainloop()
    model.plot_cost()  # 误差曲线

• 环境模块maze_env.py

import tkinter as tk
import sys
import numpy as np

UNIT = 40  # pixels
MAZE_H = 4  # grid height
MAZE_W = 4  # grid width


class Maze(tk.Tk, object):
    def __init__(self):
        print("<env init>")
        super(Maze, self).__init__()
        # 动作空间(定义智能体可选的行为),action=0-3
        self.action_space = ['u', 'd', 'l', 'r']
        # 使用变量
        self.n_actions = len(self.action_space)
        self.n_states = 2
        # 配置信息
        self.title('maze')
        self.geometry("160x160")
        # 初始化操作
        self.__build_maze()

    def render(self):
        # time.sleep(0.1)
        self.update()

    def reset(self):
        # 智能体回到初始位置
        # time.sleep(0.1)
        self.update()
        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 (np.array(self.canvas.coords(self.rect)[:2]) - np.array(self.canvas.coords(self.oval)[:2])) / (MAZE_H * UNIT)

    def step(self, action):
        # 智能体向前移动一步：返回next_state,reward,terminal
        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

        next_coords = self.canvas.coords(self.rect)  # next state

        # reward function
        if next_coords == self.canvas.coords(self.oval):
            reward = 1
            print("victory")
            done = True
        elif next_coords in [self.canvas.coords(self.hell1)]:
            reward = -1
            print("defeat")
            done = True
        else:
            reward = 0
            done = False
        s_ = (np.array(next_coords[:2]) - np.array(self.canvas.coords(self.oval)[:2])) / (MAZE_H * UNIT)
        return s_, reward, done

    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)
        origin = np.array([20, 20])
        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')
        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')
        self.rect = self.canvas.create_rectangle(
            origin[0] - 15, origin[1] - 15,
            origin[0] + 15, origin[1] + 15,
            fill='red')
        self.canvas.pack()

• DQN模型RL_brain.py

class Net(nn.Module):
    def __init__(self, n_states, n_actions):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(n_states, 10)
        self.fc2 = nn.Linear(10, n_actions)
        self.fc1.weight.data.normal_(0, 0.1)
        self.fc2.weight.data.normal_(0, 0.1)

    def forward(self, x):
        x = self.fc1(x)
        x = F.relu(x)
        out = self.fc2(x)
        return out


class DQN:
    def __init__(self, n_states, n_actions):
        print("<DQN init>")
        # DQN有两个net:target net和eval net,具有选动作，存经历，学习三个基本功能
        self.eval_net, self.target_net = Net(n_states, n_actions), Net(n_states, n_actions)
        self.loss = nn.MSELoss()
        self.optimizer = torch.optim.Adam(self.eval_net.parameters(), lr=0.01)
        self.n_actions = n_actions
        self.n_states = n_states
        # 使用变量
        self.learn_step_counter = 0  # target网络学习计数
        self.memory_counter = 0  # 记忆计数
        self.memory = np.zeros((2000, 2 * 2 + 2))  # 2*2(state和next_state,每个x,y坐标确定)+2(action和reward),存储2000个记忆体
        self.cost = []  # 记录损失值

    def choose_action(self, x, epsilon):
        # print("<choose_action>")
        x = torch.unsqueeze(torch.FloatTensor(x), 0)  # (1,2)
        if np.random.uniform() < epsilon:
            action_value = self.eval_net.forward(x)
            action = torch.max(action_value, 1)[1].data.numpy()[0]
        else:
            action = np.random.randint(0, self.n_actions)
        # print("action=", action)
        return action

    def store_transition(self, state, action, reward, next_state):
        # print("<store_transition>")
        transition = np.hstack((state, [action, reward], next_state))
        index = self.memory_counter % 200  # 满了就覆盖旧的
        self.memory[index, :] = transition
        self.memory_counter += 1

    def learn(self):
        # print("<learn>")
        # target net 更新频率,用于预测，不会及时更新参数
        if self.learn_step_counter % 100 == 0:
            self.target_net.load_state_dict((self.eval_net.state_dict()))
        self.learn_step_counter += 1

        # 使用记忆库中批量数据
        sample_index = np.random.choice(200, 16)  # 2000个中随机抽取32个作为batch_size
        memory = self.memory[sample_index, :]  # 抽取的记忆单元，并逐个提取
        state = torch.FloatTensor(memory[:, :2])
        action = torch.LongTensor(memory[:, 2:3])
        reward = torch.LongTensor(memory[:, 3:4])
        next_state = torch.FloatTensor(memory[:, 4:6])

        # 计算loss,q_eval:所采取动作的预测value,q_target:所采取动作的实际value
        q_eval = self.eval_net(state).gather(1, action) # eval_net->(64,4)->按照action索引提取出q_value
        q_next = self.target_net(next_state).detach()
        # torch.max->[values=[],indices=[]] max(1)[0]->values=[]
        q_target = reward + 0.9 * q_next.max(1)[0].unsqueeze(1) # label
        loss = self.loss(q_eval, q_target)
        self.cost.append(loss)
        # 反向传播更新
        self.optimizer.zero_grad()  # 梯度重置
        loss.backward()  # 反向求导
        self.optimizer.step()  # 更新模型参数

    def plot_cost(self):
        plt.plot(np.arange(len(self.cost)), self.cost)
        plt.xlabel("step")
        plt.ylabel("cost")
        plt.show()

参考

MorvanZhou/Reinforcement-learning-with-tensorflow
DQN

__EOF__

本文作者：Rocky
本文链接：https://www.cnblogs.com/nrocky/p/14496252.html
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