Pytorch学习笔记【15】：Q-learning强化学习算法简单实现

这个没有基础没法看的，建议没有基础的先看看我的另一篇博客，会介绍强化学习以及Q-learng算法流程：

https://blog.csdn.net/qq_36499794/article/details/103162841

 

一. 代码

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import gym

# 定义参数
BATCH_SIZE = 32             # 每一批的训练量
LR = 0.01                   # 学习率
EPSILON = 0.9               # 贪婪策略指数，Q-learning的一个指数，用于指示是探索还是利用。
GAMMA = 0.9                 # reward discount
TARGET_REPLACE_ITER = 100   # target的更新频率
MEMORY_CAPACITY = 2000
env = gym.make('CartPole-v0')
env = env.unwrapped
N_ACTIONS = env.action_space.n
N_STATES = env.observation_space.shape[0]
ENV_A_SHAPE = 0 if isinstance(env.action_space.sample(), int) else env.action_space.sample().shape     # to confirm the shape


# 创建神经网络模型，输出的是可能的动作
class Net(nn.Module):
    def __init__(self, ):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(N_STATES, 50)
        self.fc1.weight.data.normal_(0, 0.1)   # initialization
        self.out = nn.Linear(50, N_ACTIONS)
        self.out.weight.data.normal_(0, 0.1)   # initialization

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


# 创建Q-learning的模型
class DQN(object):
    def __init__(self):
        # 两张网是一样的，不过就是target_net是每100次更新一次，eval_net每次都更新
        self.eval_net, self.target_net = Net(), Net()

        self.learn_step_counter = 0                                     # 如果次数到了，更新target_net
        self.memory_counter = 0                                         # for storing memory
        self.memory = np.zeros((MEMORY_CAPACITY, N_STATES * 2 + 2))     # 初始化记忆
        self.optimizer = torch.optim.Adam(self.eval_net.parameters(), lr=LR)
        self.loss_func = nn.MSELoss()

    # 选择动作
    def choose_action(self, x):
        x = torch.unsqueeze(torch.FloatTensor(x), 0)
        # input only one sample
        if np.random.uniform() < EPSILON:   # 贪婪策略
            actions_value = self.eval_net.forward(x)
            action = torch.max(actions_value, 1)[1].data.numpy()
            action = action[0] if ENV_A_SHAPE == 0 else action.reshape(ENV_A_SHAPE)  # return the argmax index
        else:   # random
            action = np.random.randint(0, N_ACTIONS)
            action = action if ENV_A_SHAPE == 0 else action.reshape(ENV_A_SHAPE)
        return action

    # 存储记忆
    def store_transition(self, s, a, r, s_):
        transition = np.hstack((s, [a, r], s_)) # 将每个参数打包起来
        # replace the old memory with new memory
        index = self.memory_counter % MEMORY_CAPACITY
        self.memory[index, :] = transition
        self.memory_counter += 1

    def learn(self):
        # target parameter update
        if self.learn_step_counter % TARGET_REPLACE_ITER == 0:
            self.target_net.load_state_dict(self.eval_net.state_dict())
        self.learn_step_counter += 1

        # 学习过程
        sample_index = np.random.choice(MEMORY_CAPACITY, BATCH_SIZE)
        b_memory = self.memory[sample_index, :]
        b_s = torch.FloatTensor(b_memory[:, :N_STATES])
        b_a = torch.LongTensor(b_memory[:, N_STATES:N_STATES+1].astype(int))
        b_r = torch.FloatTensor(b_memory[:, N_STATES+1:N_STATES+2])
        b_s_ = torch.FloatTensor(b_memory[:, -N_STATES:])

        # q_eval w.r.t the action in experience
        q_eval = self.eval_net(b_s).gather(1, b_a)  # shape (batch, 1)
        q_next = self.target_net(b_s_).detach()     # detach的作用就是不反向传播去更新，因为target的更新在前面定义好了的
        q_target = b_r + GAMMA * q_next.max(1)[0].view(BATCH_SIZE, 1)   # shape (batch, 1)
        loss = self.loss_func(q_eval, q_target)

        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

dqn = DQN()

print('\nCollecting experience...')
for i_episode in range(400):
    s = env.reset() # 搜集当前环境状态。
    ep_r = 0
    while True:
        env.render()
        a = dqn.choose_action(s)

        # take action
        s_, r, done, info = env.step(a)

        # modify the reward
        x, x_dot, theta, theta_dot = s_
        r1 = (env.x_threshold - abs(x)) / env.x_threshold - 0.8
        r2 = (env.theta_threshold_radians - abs(theta)) / env.theta_threshold_radians - 0.5
        r = r1 + r2

        dqn.store_transition(s, a, r, s_)

        ep_r += r
        if dqn.memory_counter > MEMORY_CAPACITY:
            dqn.learn()
            if done:
                print('Ep: ', i_episode,
                      '| Ep_r: ', round(ep_r, 2))

        if done:
            break
        s = s_