强化学习-DQN(Deep Q-netword)

1.DQN的动作选择策略是epsilon-贪婪策略

(1)当随机值小于阈值时, 使用随机的action

 (2)当随机值大于阈值时, 使用网络输出的最大Q值的方向

2.DQN的损失函数计算

   (1)输入state, 生成最大方向的action

   (2)将action输入到环境中, 获得next_state, reward, done, _

   (3)如果步数大于观察的步数，从缓冲区选取数据, 进行网络的更新

           (1) 将状态输入到训练网络中, 获得当前状态动作对应的Q值

           (2)将下一个状态输入到目标网络中, 获得下一个状态对应的最大Q值

           (3)将下一个状态的最大值 加上及时的奖励，表示目标Q值

           (4)使用当前状态动作对应的Q值和目标Q值来构成mse损失

 q_values = self.q_net(states).gather(1, actions) # 获得对应动作的Q值
 max_next_q_values = self.target_q_net(next_states).max(1)[0].view(-1, 1)
 q_target = rewards + self.gamma * max_next_q_values
 dqn_loss = torch.mean(F.mse_loss(q_values, q_target))

实际的场景:

根据小车速度, 位置, 尖端速度, 杆的角度来预测车的方向，前进或者后退

train.py 用于进行模型的训练

import random
import gym
import numpy as np
import collections
import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt
from tqdm import tqdm
from modelCopy import DQN

import rl_utils


class ReplayBuffer:
    """
    用于存放观察的数据
    """
    def __init__(self, capacity):
        self.buffer = collections.deque(maxlen=capacity)

    def add(self, state, action, reward, next_state, done):
        """添加观察的数据"""
        self.buffer.append((state, action, reward, next_state, done))

    def sample(self, batch_size):
        """取一个batch_size的数据"""
        transitions = random.sample(self.buffer, batch_size)
        state, action, reward, next_state, done = zip(*transitions)
        return np.array(state), action, reward, np.array(next_state), done

    def size(self):
        return len(self.buffer)

lr = 2e-3 # 学习率
num_episodes = 500 #迭代次数
hidden_dim = 128 #隐藏层
gamma = 0.98
epsilon = 0.01
target_update = 10
batch_size = 64

device = torch.device("cuda") if torch.cuda.is_available() else torch.device('cpu')

buffer_size = 10000 # 观察数据的数量
minimal_size = 500
replay_buffer = ReplayBuffer(buffer_size)
env_name = "CartPole-v0"
env = gym.make(env_name)
state_dim = env.observation_space.shape[0] # 这里的输入是4个变量 速度, 位置, 尖端速度, 杆的角度
action_dim = env.action_space.n

agent = DQN(state_dim, hidden_dim, action_dim, lr,
            gamma, epsilon, target_update, device)

return_list = []
for i in range(10):
    with tqdm(total=int(num_episodes / 10), desc='Iteration') as pbar:
        for i_episode in range(int(num_episodes/10)):
            episode_return = 0
            state = env.reset()
            done = False
            while not done:
                action = agent.take_action(state) # 输入到网络获得最大Q值对应的动作
                next_state, reward, done, _ = env.step(action) # 将action输入到环境中, 获得对应的下一个时刻的动作, 奖励值
                replay_buffer.add(state, action, reward, next_state, done) # 在缓冲区加入数据
                state = next_state
                episode_return += reward # 将奖励值进行累加
                # 当buffer的数量超过一定数量的时候, 进行Q网络训练
                if replay_buffer.size() > minimal_size:
                    b_s, b_a, b_r, b_ns, b_d = replay_buffer.sample(batch_size=batch_size)
                    transition_dict = {
                        'states':b_s,
                        'action':b_a,
                        'next_states':b_ns,
                        'rewards': b_r,
                        'done': b_d,
                    }
                    agent.update(transition_dict)

            return_list.append(episode_return)
            if (i_episode + 1) % 10 == 0:
                pbar.set_postfix({
                    'episode':
                    '%d' % (num_episodes / 10 * i + i_episode),
                    'return':
                    '%.3f' % np.mean(return_list[-10:])
                })



episodes_list = list(range(len(return_list)))
plt.plot(episodes_list, return_list)
plt.xlabel('Episodes')
plt.ylabel('Returns')
plt.title('DQN on {}'.format(env_name))
plt.show()

mv_return = rl_utils.moving_average(return_list, 9)
plt.plot(episodes_list, mv_return)
plt.xlabel('Episodes')
plt.ylabel('Returns')
plt.title('DQN on {}'.format(env_name))
plt.show()

model.py 用于进行模型的更新