import gymnasium as gym
import random
import torch
import torch.optim as optim
import torch.nn.functional as F
import torch.nn as nn
import time

# 创建环境
#env = gym.make("CartPole-v1", render_mode="human")  # human模式会用pyglet显示窗口
env = gym.make('CartPole-v1')
# 重置环境
observation, info = env.reset()
print("初始观察值:", observation)

#记住DQN训练的是Q*，输出的也是Q*，而不是动作，动作要根据Q*判断并反馈
class DQN(nn.Module):
    def __init__(self, state_size, action_size):
        super(DQN, self).__init__()
        self.l1 = nn.Linear(state_size, 128)
        self.l3 = nn.Linear(128, 64)
        self.l4 = nn.Linear(64, 32)
        self.l5 = nn.Linear(32, action_size)

    def forward(self, x):
        x = F.relu(self.l1(x))
        x = F.relu(self.l3(x))
        x = F.relu(self.l4(x))
        x = self.l5(x)
        return x


#定义参数
epsilon = 0.95
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
gamma = 0.99
lrs = 0.005
epsilon_decay = 0.995
epsilon_min = 0.01
batch_size = 64
#memory = deque(maxlen=10000)
num_episodes = 500

#初始化
ez = DQN(state_size, action_size)
optimizer = optim.Adam(ez.parameters(), lr = lrs)
criterion = nn.MSELoss()

#训练
for i in range(500):
    state, _ = env.reset()   # gym >=0.26 返回 (obs, info)
    total_reward = 0.0
    done = False

    while not done:
        # ε-greedy 选择动作
        if random.random() < epsilon:
            action = env.action_space.sample()
        else:
            action = ez(torch.tensor(state, dtype=torch.float32)).argmax().item()

        # 与环境交互
        next_state, reward, terminated, truncated, _ = env.step(action)
        done = terminated or truncated
        total_reward += reward

        # 计算 Q(s, a)
        q_values = ez(torch.tensor(state, dtype=torch.float32))
        now_Q = q_values[action]

        # 计算 target
        next_q_value = ez(torch.tensor(next_state, dtype=torch.float32)).max().item()
        target_Q = reward + gamma * next_q_value * (0 if done else 1)

        # 计算损失
        target_Q = torch.tensor(target_Q, dtype=torch.float32)
        loss = criterion(now_Q, target_Q)

        # 更新网络
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # 状态更新
        state = next_state

    print(f"Episode {i}, total_reward = {total_reward}")

# 测试函数
def test_agent(env, model, episodes=10):
    total_rewards = []
    for ep in range(episodes):
        state, _ = env.reset()
        done = False
        total_reward = 0.0
        while not done:
            # 关闭梯度计算，加速
            with torch.no_grad():
                action = model(torch.tensor(state, dtype=torch.float32)).argmax().item()
            next_state, reward, terminated, truncated, _ = env.step(action)
            done = terminated or truncated
            state = next_state
            total_reward += reward
        total_rewards.append(total_reward)
        print(f"Test Episode {ep+1}: reward = {total_reward}")

    avg_reward = sum(total_rewards) / episodes
    print(f"Average reward over {episodes} episodes: {avg_reward}")

# 调用测试
test_agent(env, ez, episodes=10)

#最佳数据：平均150