from collections import deque
import numpy as np
import random
import torch
import torch.optim as optim
import torch.nn as nn
import gymnasium as gym
import torch.nn.functional as F


class DQN(nn.Module):
    def __init__(self, state_size, action_size):
        super(DQN, self).__init__()
        self.l1 = nn.Linear(state_size, 128)
        self.l2 = nn.Linear(128, 32)
        self.l4 = nn.Linear(32, action_size)

    def forward(self, x):
        x = F.relu(self.l1(x))
        x = F.relu(self.l2(x))
        x = self.l4(x)
        return x

lr = 0.001
gamma = 0.99
epsilon = 1.0
memory = deque(maxlen=1000000)
batch_size = 64
epsilon_decay = 0.995
epsilon_min = 0.01

env = gym.make('CartPole-v1')
state_size = env.observation_space.shape[0]
action_size = env.action_space.n


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

def replay():
    if(len(memory) < batch_size):
        return

    #随机选一组64个训练
    batch = random.sample(memory, batch_size)
    states, actions, rewards, next_states, dones = zip(*batch)
    states = torch.from_numpy(np.stack(states))  # 直接把 batch 堆成 array 再转 tensor
    next_states = torch.from_numpy(np.stack(next_states))
    rewards = torch.from_numpy(np.array(rewards, dtype=np.float32))
    actions = torch.from_numpy(np.array(actions, dtype=np.int64))
    dones = torch.from_numpy(np.array(dones, dtype=np.float32))

    q_values = ez(states).gather(1, actions.unsqueeze(1)).squeeze()
    next_q_values = ez(next_states).max(1)[0]
    target = rewards + gamma * next_q_values * (1 - dones)

    loss = criterion(q_values, target.detach())
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

for e in range(350):
    state, _ = env.reset()
    done = False
    total_reward = 0
    while not done:
        if random.random() < epsilon:
            action = env.action_space.sample()
        else:
            action = ez(torch.tensor(state, dtype=torch.float32)).argmax().item()

        next_state, reward, terminated, truncated, _ = env.step(action)
        done = terminated or truncated
        # 在存储到 memory 时：
        memory.append((np.array(state, dtype=np.float32),
                       action,
                       reward,
                       np.array(next_state, dtype=np.float32),
                       done))
        state = next_state
        total_reward += reward
        replay()

    epsilon = max(epsilon * epsilon_decay, epsilon_min)
    print(f"Episode {e + 1}: Reward = {total_reward}")

torch.save(ez.state_dict(), "cartpole_dqn.pth")
print("Model saved to cartpole_dqn.pth")

# 测试函数
def test_agent(env, model, episodes=10):
    env = gym.make('CartPole-v1', render_mode='human')
    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)