深度强化学习-D3QN算法原理与代码

Dueling Double Deep Q Network(D3QN)算法结合了Double DQN和Dueling DQN算法的思想，进一步提升了算法的性能。如果对Doubel DQN和Dueling DQN算法还不太了解的话，可以参考我的这两篇博文：深度强化学习-Doubel DQN算法原理与代码和深度强化学习-Dueling DQN算法原理与代码，分别详细讲述了这两个算法的原理以及代码实现。

indigo love

16195人浏览 · 2021-12-15 10:01:32

indigo love · 2021-12-15 10:01:32 发布

Dueling Double Deep Q Network(D3QN)算法结合了Double DQN和Dueling DQN算法的思想，进一步提升了算法的性能。如果对Doubel DQN和Dueling DQN算法还不太了解的话，可以参考我的这两篇博文：深度强化学习-Double DQN算法原理与代码和深度强化学习-Dueling DQN算法原理与代码，分别详细讲述了这两个算法的原理以及代码实现。本文就带领大家了解一下D3QN算法，代码链接见下方。

代码：https://github.com/indigoLovee/D3QN

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1 D3QN算法简介

Dueling Double Deep Q Network(D3QN)算法是在Dueling DQN算法的基础上融入了Doubel DQN算法的思想，它与Dueling DQN算法唯一的区别在于计算目标值的方式。在Dueling DQN算法中，目标值的计算方式为

$y_{t}=r_{t+1}+\gamma max_{a}q(s_{t+1},a;w_{t})$

即利用目标网络获取 $s_{t+1}$ 状态下所有动作的动作价值，然后基于最优动作价值计算目标值。由于这里的最大化操作，导致算法存在“过估计”问题，影响决策的准确性。其中 $w_{t}$ 表示目标网络参数。

在D3QN算法中，目标值的计算方式为

$y_{t}=r_{t+1}+\gamma q(s_{t+1},argmax_{a}q(s_{t+1},a;w_{e});w_{t})$

即利用评估网络获取 $s_{t+1}$ 状态下最优动作价值对应的动作，然后利用目标网络计算该动作的动作价值，从而得到目标值。通过两个网络的交互，有效避免了算法的“过估计”问题。其中 $w_{e}$ 和 $w_{t}$ 分别表示评估网络和目标网络的参数。

这其实就是D3QN算法的核心所在啦，如果已经熟悉Dueling DQN和Doubel DQN算法的话，这个算法其实是非常容易理解的。

2 D3QN算法代码

经验回放采用集中式均匀回放，代码如下(脚本buffer.py)：

import numpy as np


class ReplayBuffer:
    def __init__(self, state_dim, action_dim, max_size, batch_size):
        self.mem_size = max_size
        self.batch_size = batch_size
        self.mem_cnt = 0

        self.state_memory = np.zeros((self.mem_size, state_dim))
        self.action_memory = np.zeros((self.mem_size, ))
        self.reward_memory = np.zeros((self.mem_size, ))
        self.next_state_memory = np.zeros((self.mem_size, state_dim))
        self.terminal_memory = np.zeros((self.mem_size, ), dtype=np.bool)

    def store_transition(self, state, action, reward, state_, done):
        mem_idx = self.mem_cnt % self.mem_size

        self.state_memory[mem_idx] = state
        self.action_memory[mem_idx] = action
        self.reward_memory[mem_idx] = reward
        self.next_state_memory[mem_idx] = state_
        self.terminal_memory[mem_idx] = done

        self.mem_cnt += 1

    def sample_buffer(self):
        mem_len = min(self.mem_size, self.mem_cnt)

        batch = np.random.choice(mem_len, self.batch_size, replace=False)

        states = self.state_memory[batch]
        actions = self.action_memory[batch]
        rewards = self.reward_memory[batch]
        states_ = self.next_state_memory[batch]
        terminals = self.terminal_memory[batch]

        return states, actions, rewards, states_, terminals

    def ready(self):
        return self.mem_cnt > self.batch_size

目标网络的更新方式为软更新，D3QN算法的实现代码如下(脚本D3QN.py)：

import torch as T
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
from buffer import ReplayBuffer

device = T.device("cuda:0" if T.cuda.is_available() else "cpu")


class DuelingDeepQNetwork(nn.Module):
    def __init__(self, alpha, state_dim, action_dim, fc1_dim, fc2_dim):
        super(DuelingDeepQNetwork, self).__init__()

        self.fc1 = nn.Linear(state_dim, fc1_dim)
        self.fc2 = nn.Linear(fc1_dim, fc2_dim)
        self.V = nn.Linear(fc2_dim, 1)
        self.A = nn.Linear(fc2_dim, action_dim)

        self.optimizer = optim.Adam(self.parameters(), lr=alpha)
        self.to(device)

    def forward(self, state):
        x = T.relu(self.fc1(state))
        x = T.relu(self.fc2(x))

        V = self.V(x)
        A = self.A(x)
        Q = V + A - T.mean(A, dim=-1, keepdim=True)

        return Q

    def save_checkpoint(self, checkpoint_file):
        T.save(self.state_dict(), checkpoint_file)

    def load_checkpoint(self, checkpoint_file):
        self.load_state_dict(T.load(checkpoint_file))


class D3QN:
    def __init__(self, alpha, state_dim, action_dim, fc1_dim, fc2_dim, ckpt_dir,
                 gamma=0.99, tau=0.005, epsilon=1.0, eps_end=0.01, eps_dec=5e-7,
                 max_size=1000000, batch_size=256):
        self.gamma = gamma
        self.tau = tau
        self.epsilon = epsilon
        self.eps_min = eps_end
        self.eps_dec = eps_dec
        self.batch_size = batch_size
        self.checkpoint_dir = ckpt_dir
        self.action_space = [i for i in range(action_dim)]

        self.q_eval = DuelingDeepQNetwork(alpha=alpha, state_dim=state_dim, action_dim=action_dim,
                                          fc1_dim=fc1_dim, fc2_dim=fc2_dim)
        self.q_target = DuelingDeepQNetwork(alpha=alpha, state_dim=state_dim, action_dim=action_dim,
                                            fc1_dim=fc1_dim, fc2_dim=fc2_dim)

        self.memory = ReplayBuffer(state_dim=state_dim, action_dim=action_dim,
                                   max_size=max_size, batch_size=batch_size)

        self.update_network_parameters(tau=1.0)

    def update_network_parameters(self, tau=None):
        if tau is None:
            tau = self.tau

        for q_target_params, q_eval_params in zip(self.q_target.parameters(), self.q_eval.parameters()):
            q_target_params.data.copy_(tau * q_eval_params + (1 - tau) * q_target_params)

    def remember(self, state, action, reward, state_, done):
        self.memory.store_transition(state, action, reward, state_, done)

    def decrement_epsilon(self):
        self.epsilon = self.epsilon - self.eps_dec \
            if self.epsilon > self.eps_min else self.eps_min

    def choose_action(self, observation, isTrain=True):
        state = T.tensor([observation], dtype=T.float).to(device)
        q_vals = self.q_eval.forward(state)
        action = T.argmax(q_vals).item()

        if (np.random.random() < self.epsilon) and isTrain:
            action = np.random.choice(self.action_space)

        return action

    def learn(self):
        if not self.memory.ready():
            return

        states, actions, rewards, next_states, terminals = self.memory.sample_buffer()
        batch_idx = T.arange(self.batch_size, dtype=T.long).to(device)
        states_tensor = T.tensor(states, dtype=T.float).to(device)
        actions_tensor = T.tensor(actions, dtype=T.long).to(device)
        rewards_tensor = T.tensor(rewards, dtype=T.float).to(device)
        next_states_tensor = T.tensor(next_states, dtype=T.float).to(device)
        terminals_tensor = T.tensor(terminals).to(device)

        with T.no_grad():
            q_ = self.q_target.forward(next_states_tensor)
            max_actions = T.argmax(self.q_eval.forward(next_states_tensor), dim=-1)
            q_[terminals_tensor] = 0.0
            target = rewards_tensor + self.gamma * q_[batch_idx, max_actions]
        q = self.q_eval.forward(states_tensor)[batch_idx, actions_tensor]

        loss = F.mse_loss(q, target.detach())
        self.q_eval.optimizer.zero_grad()
        loss.backward()
        self.q_eval.optimizer.step()

        self.update_network_parameters()
        self.decrement_epsilon()

    def save_models(self, episode):
        self.q_eval.save_checkpoint(self.checkpoint_dir + 'Q_eval/D3QN_q_eval_{}.pth'.format(episode))
        print('Saving Q_eval network successfully!')
        self.q_target.save_checkpoint(self.checkpoint_dir + 'Q_target/D3QN_Q_target_{}.pth'.format(episode))
        print('Saving Q_target network successfully!')

    def load_models(self, episode):
        self.q_eval.load_checkpoint(self.checkpoint_dir + 'Q_eval/D3QN_q_eval_{}.pth'.format(episode))
        print('Loading Q_eval network successfully!')
        self.q_target.load_checkpoint(self.checkpoint_dir + 'Q_target/D3QN_Q_target_{}.pth'.format(episode))
        print('Loading Q_target network successfully!')

算法仿真环境为gym库中的LunarLander-v2，因此需要先配置好gym库。进入Anaconda3中对应的Python环境中，执行下面的指令

pip install gym

但是，这样安装的gym库只包括少量的内置环境，如算法环境、简单文字游戏和经典控制环境，无法使用LunarLander-v2。因此还需要安装一些其他依赖项，具体可以参考我的这篇博文：AttributeError: module ‘gym.envs.box2d‘ has no attribute ‘LunarLander‘ 解决办法

让智能体在环境中训练500轮，训练代码如下(脚本train.py)：

import gym
import numpy as np
import argparse
from utils import create_directory, plot_learning_curve
from D3QN import D3QN

parser = argparse.ArgumentParser()
parser.add_argument('--max_episodes', type=int, default=500)
parser.add_argument('--ckpt_dir', type=str, default='./checkpoints/D3QN/')
parser.add_argument('--reward_path', type=str, default='./output_images/reward.png')
parser.add_argument('--epsilon_path', type=str, default='./output_images/epsilon.png')

args = parser.parse_args()


def main():
    env = gym.make('LunarLander-v2')
    agent = D3QN(alpha=0.0003, state_dim=env.observation_space.shape[0], action_dim=env.action_space.n,
                 fc1_dim=256, fc2_dim=256, ckpt_dir=args.ckpt_dir, gamma=0.99, tau=0.005, epsilon=1.0,
                 eps_end=0.05, eps_dec=5e-4, max_size=1000000, batch_size=256)
    create_directory(args.ckpt_dir, sub_dirs=['Q_eval', 'Q_target'])
    total_rewards, avg_rewards, epsilon_history = [], [], []

    for episode in range(args.max_episodes):
        total_reward = 0
        done = False
        observation = env.reset()
        while not done:
            action = agent.choose_action(observation, isTrain=True)
            observation_, reward, done, info = env.step(action)
            agent.remember(observation, action, reward, observation_, done)
            agent.learn()
            total_reward += reward
            observation = observation_

        total_rewards.append(total_reward)
        avg_reward = np.mean(total_rewards[-100:])
        avg_rewards.append(avg_reward)
        epsilon_history.append(agent.epsilon)
        print('EP:{} Reward:{} Avg_reward:{} Epsilon:{}'.
              format(episode+1, total_reward, avg_reward, agent.epsilon))

        if (episode + 1) % 50 == 0:
            agent.save_models(episode+1)

    episodes = [i+1 for i in range(args.max_episodes)]
    plot_learning_curve(episodes, avg_rewards, title='Reward', ylabel='reward',
                        figure_file=args.reward_path)
    plot_learning_curve(episodes, epsilon_history, title='Epsilon', ylabel='epsilon',
                        figure_file=args.epsilon_path)


if __name__ == '__main__':
    main()

训练时还会用到画图函数和创建文件夹函数，它们均放置在utils.py脚本中，具体代码如下：

import os
import matplotlib.pyplot as plt


def create_directory(path: str, sub_dirs: list):
    for sub_dir in sub_dirs:
        if os.path.exists(path + sub_dir):
            print(path + sub_dir + 'is already exist!')
        else:
            os.makedirs(path + sub_dir, exist_ok=True)
            print(path + sub_dir + 'create successfully!')


def plot_learning_curve(episodes, records, title, ylabel, figure_file):
    plt.figure()
    plt.plot(episodes, records, linestyle='-', color='r')
    plt.title(title)
    plt.xlabel('episode')
    plt.ylabel(ylabel)

    plt.show()
    plt.savefig(figure_file)

仿真结果如下图所示：

平均累积奖励曲线