deep_q_learning

import time
from collections import deque, namedtuple

import gymnasium as gym
import numpy as np
import PIL.Image
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F

import utils

# 设置随机种子
torch.manual_seed(utils.SEED)
np.random.seed(utils.SEED)

MEMORY_SIZE = 100_000     # 记忆缓冲区大小
GAMMA = 0.995             # 折扣因子
ALPHA = 1e-3              # 学习率
NUM_STEPS_FOR_UPDATE = 4  # 每C步执行一次学习更新

# 创建环境时指定渲染模式为rgb_array
env = gym.make('LunarLander-v3', render_mode='rgb_array')

# 直接渲染，无需指定mode参数
env.reset()
PIL.Image.fromarray(env.render())

state_size = env.observation_space.shape
num_actions = env.action_space.n

print('状态形状:', state_size)
print('动作数量:', num_actions)

# 重置环境并获取初始状态（注意：Gymnasium的reset返回(state, info)元组）
initial_state, _ = env.reset()

# 选择一个动作
action = 0

# 用给定的动作运行环境动态的单个时间步
next_state, reward, done, truncated, info = env.step(action)
# 合并done和truncated（如果需要）
done = done or truncated

with np.printoptions(formatter={'float': '{:.3f}'.format}):
    print("初始状态:", initial_state)
    print("动作:", action)
    print("下一状态:", next_state)
    print("获得的奖励:", reward)
    print(" episode是否终止:", done)
    print("信息:", info)


# 创建Q网络
class QNetwork(nn.Module):
    def __init__(self, state_size, num_actions):
        super(QNetwork, self).__init__()
        self.fc1 = nn.Linear(state_size[0], 64)
        self.fc2 = nn.Linear(64, 64)
        self.fc3 = nn.Linear(64, num_actions)
        
    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        return self.fc3(x)

# 初始化Q网络和目标Q网络
q_network = QNetwork(state_size, num_actions)
target_q_network = QNetwork(state_size, num_actions)

# 复制Q网络权重到目标Q网络
target_q_network.load_state_dict(q_network.state_dict())
target_q_network.eval()  # 目标网络设为评估模式

# 定义优化器
optimizer = optim.Adam(q_network.parameters(), lr=ALPHA)

# 单元测试
from public_tests import *

test_network_pytorch(q_network)
test_network_pytorch(target_q_network)
test_optimizer_pytorch(optimizer, ALPHA)

# 用命名元组存储经验
Experience = namedtuple("Experience", field_names=["state", "action", "reward", "next_state", "done"])

# 计算损失的函数
def compute_loss(experiences, gamma, q_network, target_q_network):
    """
    计算损失
    
    参数:
      experiences: (元组) 包含["state", "action", "reward", "next_state", "done"]的命名元组
      gamma: (float) 折扣因子
      q_network: (PyTorch模型) 预测q值的模型
      target_q_network: (PyTorch模型) 预测目标值的模型
          
    返回:
      loss: (PyTorch张量) y目标与Q(s,a)值之间的均方误差
    """
    
    # 解包小批量经验元组
    states, actions, rewards, next_states, done_vals = experiences
    
    # 转换为PyTorch张量
    states = torch.FloatTensor(states)
    actions = torch.LongTensor(actions).unsqueeze(1)  # 增加维度以便gather操作
    rewards = torch.FloatTensor(rewards)
    next_states = torch.FloatTensor(next_states)
    done_vals = torch.FloatTensor(done_vals)
    
    # 计算max Q^(s,a)
    with torch.no_grad():  # 目标网络不计算梯度
        max_qsa = target_q_network(next_states).max(1)[0]
    
    # 设置y = R如果episode终止，否则y = R + γ max Q^(s,a)
    y_targets = rewards + (1 - done_vals) * gamma * max_qsa
    
    # 获取q值
    q_values = q_network(states)
    q_values = q_values.gather(1, actions).squeeze(1)  # 选择对应动作的Q值
    
    # 计算损失
    loss = F.mse_loss(q_values, y_targets)
    
    return loss

# 单元测试
test_compute_loss_pytorch(compute_loss)

def agent_learn(experiences, gamma):
    """
    更新Q网络的权重
    
    参数:
      experiences: (元组) 包含["state", "action", "reward", "next_state", "done"]的命名元组
      gamma: (float) 折扣因子
    """
    
    # 计算损失
    loss = compute_loss(experiences, gamma, q_network, target_q_network)
    
    # 清零梯度
    optimizer.zero_grad()
    
    # 反向传播
    loss.backward()
    
    # 更新权重
    optimizer.step()
    
    # 更新目标Q网络的权重
    utils.update_target_network_pytorch(q_network, target_q_network)

start = time.time()

num_episodes = 2000
max_num_timesteps = 1000

total_point_history = []

num_p_av = 100    # 用于平均的总点数
epsilon = 1.0     # ε-贪婪策略的初始ε值

# 创建容量为N的记忆缓冲区D
memory_buffer = deque(maxlen=MEMORY_SIZE)

for i in range(num_episodes):
    
    # 重置环境到初始状态并获取初始状态（Gymnasium返回(state, info)）
    state, _ = env.reset()
    total_points = 0
    
    for t in range(max_num_timesteps):
        
        # 从当前状态S使用ε-贪婪策略选择动作A
        state_tensor = torch.FloatTensor(state).unsqueeze(0)  # 状态需要符合q_network的输入形状
        with torch.no_grad():
            q_values = q_network(state_tensor)
        action = utils.get_action(q_values.numpy(), epsilon)
        
        # 执行动作A并接收奖励R和下一状态S'（Gymnasium的step返回5个值）
        next_state, reward, done, truncated, _ = env.step(action)
        # 合并done和truncated
        done = done or truncated
        
        # 将经验元组(S,A,R,S')存储在记忆缓冲区中
        memory_buffer.append(Experience(state, action, reward, next_state, done))
        
        # 仅每NUM_STEPS_FOR_UPDATE时间步更新网络
        update = utils.check_update_conditions(t, NUM_STEPS_FOR_UPDATE, memory_buffer)
        
        if update:
            # 从D中随机采样小批量经验元组(S,A,R,S')
            experiences = utils.get_experiences(memory_buffer)
            
            # 设置y目标，执行梯度下降步骤，并更新网络权重
            agent_learn(experiences, GAMMA)
        
        state = next_state.copy()
        total_points += reward
        
        if done:
            break
            
    total_point_history.append(total_points)
    av_latest_points = np.mean(total_point_history[-num_p_av:]) if i >= num_p_av else np.mean(total_point_history)
    
    # 更新ε值
    epsilon = utils.get_new_eps(epsilon)

    print(f"\rEpisode {i+1} | 最近{num_p_av}个episode的平均得分: {av_latest_points:.2f}", end="")

    if (i+1) % num_p_av == 0:
        print(f"\rEpisode {i+1} | 最近{num_p_av}个episode的平均得分: {av_latest_points:.2f}")

    # 如果最近100个episode的平均得分为200分，我们认为环境被解决了
    if av_latest_points >= 200.0 and i >= num_p_av:
        print(f"\n\n环境在{i+1}个episode内被解决!")
        torch.save(q_network.state_dict(), 'lunar_lander_model.pth')
        break
        
tot_time = time.time() - start

print(f"\n总运行时间: {tot_time:.2f}秒 ({(tot_time/60):.2f}分钟)")

# 绘制得分历史
utils.plot_history(total_point_history)

# 抑制imageio的警告
import logging
logging.getLogger().setLevel(logging.ERROR)

filename = "./videos/lunar_lander.mp4"

utils.create_video_pytorch(filename, env, q_network)
utils.embed_mp4(filename)

状态形状: (8,)
动作数量: 4
初始状态: [0.001 1.417 0.062 0.249 -0.001 -0.014 0.000 0.000]
动作: 0
下一状态: [0.001 1.422 0.062 0.224 -0.001 -0.014 0.000 0.000]
获得的奖励: 1.9140130099486612
 episode是否终止: False
信息: {}
所有测试通过！
所有测试通过！
所有测试通过！
所有测试通过！
Episode 100 | 最近100个episode的平均得分: -172.18
Episode 200 | 最近100个episode的平均得分: -110.39
Episode 300 | 最近100个episode的平均得分: -33.680
Episode 400 | 最近100个episode的平均得分: -12.89
Episode 500 | 最近100个episode的平均得分: 99.712
Episode 600 | 最近100个episode的平均得分: 190.32
Episode 630 | 最近100个episode的平均得分: 200.17

环境在630个episode内被解决!

总运行时间: 1090.56秒 (18.18分钟)

<video width="840" height="480" controls>
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