一、倒立摆问题介绍

Agent 必须在两个动作之间做出决定 - 向左或向右移动推车 - 以使连接到它的杆保持直立。

二、竞争深度Q网络简介

较于原来的 深度 $\mathrm{Q}$ 网络，它唯一的差别是改 变了网络的架构。 $Q$ 网络输入状态，输出的是每一个动作的 $Q$ 值。如下图所示，原来的深度 $Q$ 网络直接输出 $Q$ 值，竞争深度 $Q$ 网络不直接输出 $Q$ 值，而是分成两条路径运算。第一条路径会 输出一个标量 $V(s)$ ，因为它与输入 $s$ 是有关系的，所以称为 $V(s)$ 。第二条路径会输出一个 向量 $\mathbf{A}(\mathbf{s},\mathbf{a})$ ，它的每一个动作都有一个值。我们再把 $V(\mathbf{s})$ 和 $\mathbf{A}(\mathbf{s},\mathbf{a})$ 加起来就可以得到 $\mathrm{Q}$ 值 $\mathbf{Q}(\mathrm{s},\mathbf{a})$ 。

三、详细资料

关于更加详细的竞争深度Q网络的介绍，请看我之前发的博客：【EasyRL学习笔记】第七章 深度Q网络进阶技巧(Double-DQN、Dueling-DQN、Noisy-DQN、Distributional-DQN、Rainbow-DQN)

在学习竞争深度Q网络前你最好能了解以下知识点：

• 深度Q网络

四、Python代码实战

4.1 运行前配置

准备好一个RL_Utils.py文件，文件内容可以从我的一篇里博客获取：【RL工具类】强化学习常用函数工具类（Python代码）

这一步很重要，后面需要引入该RL_Utils.py文件

4.2 主要代码

import argparse
import datetime
import time
import math
import torch.optim as optim
import gym
from torch import nn

# 这里需要改成自己的RL_Utils.py文件的路径
from torch.autograd import Variable

from Python.ReinforcementLearning.EasyRL.RL_Utils import *


# 竞争深度Q网络的预测网络
class DuelingNet(nn.Module):
    def __init__(self, n_states, n_actions, hidden_size=128):
        super(DuelingNet, self).__init__()

        # 隐藏层
        self.hidden = nn.Sequential(
            nn.Linear(n_states, hidden_size),
            nn.ReLU()
        )

        # 优势函数
        self.advantage = nn.Sequential(
            nn.Linear(hidden_size, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, n_actions)
        )

        # 价值函数
        self.value = nn.Sequential(
            nn.Linear(hidden_size, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, 1)
        )

    def forward(self, x):
        x = self.hidden(x)
        advantage = self.advantage(x)
        value = self.value(x)
        return value + advantage - advantage.mean()

    def act(self, state, epsilon):
        if random.random() > epsilon:
            with torch.no_grad():
                state = Variable(torch.FloatTensor(state).unsqueeze(0))
                q_value = self.forward(state)
                action = q_value.max(1)[1].item()
        else:
            action = random.randrange(env.action_space.n)
        return action


# 经验回放缓存区
class ReplayBuffer:
    def __init__(self, capacity):
        self.capacity = capacity  # 经验回放的容量
        self.buffer = []  # 缓冲区
        self.position = 0

    def push(self, state, action, reward, next_state, done):
        ''' 缓冲区是一个队列，容量超出时去掉开始存入的转移(transition)
        '''
        if len(self.buffer) < self.capacity:
            self.buffer.append(None)
        self.buffer[self.position] = (state, action, reward, next_state, done)
        self.position = (self.position + 1) % self.capacity

    def sample(self, batch_size):
        batch = random.sample(self.buffer, batch_size)  # 随机采出小批量转移
        state, action, reward, next_state, done = zip(*batch)  # 解压成状态，动作等
        return state, action, reward, next_state, done

    def __len__(self):
        ''' 返回当前存储的量
        '''
        return len(self.buffer)


# Dueling DQN智能体对象
class DuelingDQN:
    def __init__(self, model, memory, cfg):

        self.n_actions = cfg['n_actions']
        self.device = torch.device(cfg['device'])
        self.gamma = cfg['gamma']
        ## e-greedy 探索策略参数
        self.sample_count = 0  # 采样次数
        self.epsilon = cfg['epsilon_start']
        self.sample_count = 0
        self.epsilon_start = cfg['epsilon_start']
        self.epsilon_end = cfg['epsilon_end']
        self.epsilon_decay = cfg['epsilon_decay']
        self.batch_size = cfg['batch_size']
        self.policy_net = model.to(self.device)
        self.target_net = model.to(self.device)
        # 初始化的时候，目标Q网络和估计Q网络相等，将策略网络的参数复制给目标网络
        self.target_net.load_state_dict(self.policy_net.state_dict())

        self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg['lr'])
        self.memory = memory
        self.update_flag = False

    # 训练过程采样：e-greedy policy
    def sample_action(self, state):
        self.sample_count += 1
        self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
                       math.exp(-1. * self.sample_count / self.epsilon_decay)
        if random.random() > self.epsilon:
            return self.predict_action(state)
        else:
            action = random.randrange(self.n_actions)
        return action

    # 测试过程：以最大Q值选取动作
    def predict_action(self, state):
        with torch.no_grad():
            state = torch.tensor(state, device=self.device, dtype=torch.float32).unsqueeze(dim=0)
            q_values = self.policy_net(state)
            action = q_values.max(1)[1].item()
        return action

    def update(self):
        # 当经验缓存区没有满的时候，不进行更新
        if len(self.memory) < self.batch_size:
            return
        else:
            if not self.update_flag:
                print("Begin to update!")
                self.update_flag = True
        # 从经验缓存区随机取出一个batch的数据
        state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(
            self.batch_size)
        # 将数据转化成Tensor格式
        state_batch = torch.tensor(np.array(state_batch), device=self.device,
                                   dtype=torch.float)  # shape(batchsize,n_states)
        action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(1)  # shape(batchsize,1)
        reward_batch = torch.tensor(reward_batch, device=self.device, dtype=torch.float).unsqueeze(
            1)  # shape(batchsize,1)
        next_state_batch = torch.tensor(np.array(next_state_batch), device=self.device,
                                        dtype=torch.float)  # shape(batchsize,n_states)
        done_batch = torch.tensor(np.float32(done_batch), device=self.device).unsqueeze(1)  # shape(batchsize,1)
        # 计算Q估计
        q_value_batch = self.policy_net(state_batch).gather(dim=1,
                                                            index=action_batch)  # shape(batchsize,1),requires_grad=True

        # gather函数的功能可以解释为根据 index 参数（即是索引）返回数组里面对应位置的值 ， 第一个参数为1代表按列索引，为0代表按行索引
        # unsqueeze函数起到了升维的作用，例如 torch.Size([6]):tensor([0, 1, 2, 3, 4, 5]).unsqueeze(0) => torch.Size([1, 6])
        # torch.max(tensorData,dim) 返回输入张量给定维度上每行的最大值，并同时返回每个最大值的位置索引。
        # .detach()： 输入一个张量，返回一个不具有梯度的张量（返回的张量将永久失去梯度，即使修改其requires_grad属性也无法改变）
        next_max_q_value_batch = self.policy_net(next_state_batch).max(1)[0].detach().unsqueeze(1)

        # 计算Q现实
        expected_q_value_batch = reward_batch + self.gamma * next_max_q_value_batch * (1 - done_batch)
        # 计算损失函数MSE（Q估计，Q现实）
        loss = nn.MSELoss()(q_value_batch, expected_q_value_batch)
        # 梯度下降
        self.optimizer.zero_grad()
        loss.backward()
        # 限制梯度的范围，以避免梯度爆炸
        for param in self.policy_net.parameters():
            param.grad.data.clamp_(-1.0, 1.0)
        self.optimizer.step()

    def save_model(self, path):
        Path(path).mkdir(parents=True, exist_ok=True)
        torch.save(self.target_net.state_dict(), f"{path}/checkpoint.pt")

    def load_model(self, path):
        self.target_net.load_state_dict(torch.load(f"{path}/checkpoint.pt"))
        for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
            param.data.copy_(target_param.data)


# 训练函数
def train(arg_dict, env, agent):
    # 开始计时
    startTime = time.time()
    print(f"环境名: {arg_dict['env_name']}, 算法名: {arg_dict['algo_name']}, Device: {arg_dict['device']}")
    print("开始训练智能体......")
    rewards = []
    steps = []
    for i_ep in range(arg_dict["train_eps"]):
        ep_reward = 0
        ep_step = 0
        state = env.reset()
        for _ in range(arg_dict['ep_max_steps']):
            # 画图
            if arg_dict['train_render']:
                env.render()
            ep_step += 1
            action = agent.sample_action(state)
            next_state, reward, done, _ = env.step(action)
            agent.memory.push(state, action, reward,
                              next_state, done)
            state = next_state
            agent.update()
            ep_reward += reward
            if done:
                break
        # 目标网络更新
        if (i_ep + 1) % arg_dict["target_update"] == 0:
            agent.target_net.load_state_dict(agent.policy_net.state_dict())
        steps.append(ep_step)
        rewards.append(ep_reward)
        if (i_ep + 1) % 10 == 0:
            print(f'Episode: {i_ep + 1}/{arg_dict["train_eps"]}, Reward: {ep_reward:.2f}: Epislon: {agent.epsilon:.3f}')
    print('训练结束 , 用时: ' + str(time.time() - startTime) + " s")
    # 关闭环境
    env.close()
    return {'episodes': range(len(rewards)), 'rewards': rewards}


# 测试函数
def test(arg_dict, env, agent):
    startTime = time.time()
    print("开始测试智能体......")
    print(f"环境名: {arg_dict['env_name']}, 算法名: {arg_dict['algo_name']}, Device: {arg_dict['device']}")
    rewards = []
    steps = []
    for i_ep in range(arg_dict['test_eps']):
        ep_reward = 0
        ep_step = 0
        state = env.reset()
        for _ in range(arg_dict['ep_max_steps']):
            # 画图
            if arg_dict['test_render']:
                env.render()
            ep_step += 1
            action = agent.predict_action(state)
            next_state, reward, done, _ = env.step(action)
            state = next_state
            ep_reward += reward
            if done:
                break
        steps.append(ep_step)
        rewards.append(ep_reward)
        print(f"Episode: {i_ep + 1}/{arg_dict['test_eps']}，Reward: {ep_reward:.2f}")
    print("测试结束 , 用时: " + str(time.time() - startTime) + " s")
    env.close()
    return {'episodes': range(len(rewards)), 'rewards': rewards}


# 创建环境和智能体
def create_env_agent(arg_dict):
    # 创建环境
    env = gym.make(arg_dict['env_name'])
    # 设置随机种子
    all_seed(env, seed=arg_dict["seed"])
    # 获取状态数
    try:
        n_states = env.observation_space.n
    except AttributeError:
        n_states = env.observation_space.shape[0]
    # 获取动作数
    n_actions = env.action_space.n
    print(f"状态数: {n_states}, 动作数: {n_actions}")
    # 将状态数和动作数加入算法参数字典
    arg_dict.update({"n_states": n_states, "n_actions": n_actions})
    # 实例化智能体对象
    # Q网络模型
    model = DuelingNet(n_states, n_actions, arg_dict["hidden_dim"])
    # 回放缓存区对象
    memory = ReplayBuffer(arg_dict["memory_capacity"])
    # 智能体
    agent = DuelingDQN(model, memory, arg_dict)
    # 返回环境，智能体
    return env, agent


if __name__ == '__main__':
    # 防止报错 OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
    os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
    # 获取当前路径
    curr_path = os.path.dirname(os.path.abspath(__file__))
    # 获取当前时间
    curr_time = datetime.datetime.now().strftime("%Y_%m_%d-%H_%M_%S")
    # 相关参数设置
    parser = argparse.ArgumentParser(description="hyper parameters")
    parser.add_argument('--algo_name', default='Dueling DQN', type=str, help="name of algorithm")
    parser.add_argument('--env_name', default='CartPole-v0', type=str, help="name of environment")
    parser.add_argument('--train_eps', default=200, type=int, help="episodes of training")
    parser.add_argument('--test_eps', default=20, type=int, help="episodes of testing")
    parser.add_argument('--ep_max_steps', default=100000, type=int,
                        help="steps per episode, much larger value can simulate infinite steps")
    parser.add_argument('--gamma', default=0.95, type=float, help="discounted factor")
    parser.add_argument('--epsilon_start', default=0.95, type=float, help="initial value of epsilon")
    parser.add_argument('--epsilon_end', default=0.01, type=float, help="final value of epsilon")
    parser.add_argument('--epsilon_decay', default=500, type=int,
                        help="decay rate of epsilon, the higher value, the slower decay")
    parser.add_argument('--lr', default=0.0001, type=float, help="learning rate")
    parser.add_argument('--memory_capacity', default=100000, type=int, help="memory capacity")
    parser.add_argument('--batch_size', default=64, type=int)
    parser.add_argument('--target_update', default=4, type=int)
    parser.add_argument('--hidden_dim', default=256, type=int)
    parser.add_argument('--device', default='cpu', type=str, help="cpu or cuda")
    parser.add_argument('--seed', default=520, type=int, help="seed")
    parser.add_argument('--show_fig', default=False, type=bool, help="if show figure or not")
    parser.add_argument('--save_fig', default=True, type=bool, help="if save figure or not")
    parser.add_argument('--train_render', default=False, type=bool,
                        help="Whether to render the environment during training")
    parser.add_argument('--test_render', default=True, type=bool,
                        help="Whether to render the environment during testing")
    args = parser.parse_args()
    default_args = {'result_path': f"{curr_path}/outputs/{args.env_name}/{curr_time}/results/",
                    'model_path': f"{curr_path}/outputs/{args.env_name}/{curr_time}/models/",
                    }
    # 将参数转化为字典 type(dict)
    arg_dict = {**vars(args), **default_args}
    print("算法参数字典:", arg_dict)

    # 创建环境和智能体
    env, agent = create_env_agent(arg_dict)
    # 传入算法参数、环境、智能体，然后开始训练
    res_dic = train(arg_dict, env, agent)
    print("算法返回结果字典:", res_dic)
    # 保存相关信息
    agent.save_model(path=arg_dict['model_path'])
    save_args(arg_dict, path=arg_dict['result_path'])
    save_results(res_dic, tag='train', path=arg_dict['result_path'])
    plot_rewards(res_dic['rewards'], arg_dict, path=arg_dict['result_path'], tag="train")

    # =================================================================================================
    # 创建新环境和智能体用来测试
    print("=" * 300)
    env, agent = create_env_agent(arg_dict)
    # 加载已保存的智能体
    agent.load_model(path=arg_dict['model_path'])
    res_dic = test(arg_dict, env, agent)
    save_results(res_dic, tag='test', path=arg_dict['result_path'])
    plot_rewards(res_dic['rewards'], arg_dict, path=arg_dict['result_path'], tag="test")

4.3 运行结果展示

由于有些输出太长，下面仅展示部分输出

状态数: 4, 动作数: 2
环境名: CartPole-v0, 算法名: Dueling DQN, Device: cpu
开始训练智能体......
Begin to update!
Episode: 10/200, Reward: 18.00: Epislon: 0.686
Episode: 20/200, Reward: 13.00: Epislon: 0.532
Episode: 30/200, Reward: 32.00: Epislon: 0.375
Episode: 40/200, Reward: 96.00: Epislon: 0.110
Episode: 50/200, Reward: 186.00: Epislon: 0.013
Episode: 60/200, Reward: 200.00: Epislon: 0.010
Episode: 70/200, Reward: 200.00: Epislon: 0.010
Episode: 80/200, Reward: 200.00: Epislon: 0.010
Episode: 90/200, Reward: 200.00: Epislon: 0.010
Episode: 100/200, Reward: 200.00: Epislon: 0.010
Episode: 110/200, Reward: 200.00: Epislon: 0.010
Episode: 120/200, Reward: 200.00: Epislon: 0.010
Episode: 130/200, Reward: 196.00: Epislon: 0.010
Episode: 140/200, Reward: 200.00: Epislon: 0.010
Episode: 150/200, Reward: 190.00: Epislon: 0.010
Episode: 160/200, Reward: 200.00: Epislon: 0.010
Episode: 170/200, Reward: 186.00: Epislon: 0.010
Episode: 180/200, Reward: 200.00: Epislon: 0.010
Episode: 190/200, Reward: 200.00: Epislon: 0.010
Episode: 200/200, Reward: 188.00: Epislon: 0.010
训练结束 , 用时: 201.09354186058044 s
============================================================================================================================================================================================================================================================================================================
状态数: 4, 动作数: 2
开始测试智能体......
环境名: CartPole-v0, 算法名: Dueling DQN, Device: cpu
Episode: 1/20，Reward: 200.00
Episode: 2/20，Reward: 200.00
Episode: 3/20，Reward: 200.00
Episode: 4/20，Reward: 200.00
Episode: 5/20，Reward: 200.00
Episode: 6/20，Reward: 200.00
Episode: 7/20，Reward: 200.00
Episode: 8/20，Reward: 200.00
Episode: 9/20，Reward: 200.00
Episode: 10/20，Reward: 200.00
Episode: 11/20，Reward: 200.00
Episode: 12/20，Reward: 200.00
Episode: 13/20，Reward: 200.00
Episode: 14/20，Reward: 200.00
Episode: 15/20，Reward: 200.00
Episode: 16/20，Reward: 200.00
Episode: 17/20，Reward: 200.00
Episode: 18/20，Reward: 197.00
Episode: 19/20，Reward: 200.00
Episode: 20/20，Reward: 200.00
测试结束 , 用时: 24.865518808364868 s

4.4 关于可视化的设置

如果你觉得可视化比较耗时，你可以进行设置，取消可视化。
或者你想看看训练过程的可视化，也可以进行相关设置