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9-前沿应用/9.02-强化学习基础2/supermariao.py

+import gym
+import gym
+import numpy as np
+import matplotlib.pyplot as plt
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+
+# 定义DQN网络
+class DQN(nn.Module):
+    def __init__(self, state_size, action_size):
+        super(DQN, self).__init__()
+        self.fc1 = nn.Linear(state_size, 64)
+        self.fc2 = nn.Linear(64, 64)
+        self.fc3 = nn.Linear(64, action_size)
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+# 定义经验回放缓冲区
+class ReplayBuffer:
+    def __init__(self, capacity):
+        self.capacity = capacity
+        self.buffer = []
+        self.position = 0
+
+    def push(self, state, action, reward, next_state, done):
+        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 = np.random.choice(self.buffer, batch_size, replace=False)
+        states, actions, rewards, next_states, dones = zip(*batch)
+        return states, actions, rewards, next_states, dones
+
+    def __len__(self):
+        return len(self.buffer)
+
+# 定义DQN Agent
+class DQNAgent:
+    def __init__(self, state_size, action_size):
+        self.state_size = state_size
+        self.action_size = action_size
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+        self.model = DQN(state_size, action_size).to(self.device)
+        self.target_model = DQN(state_size, action_size).to(self.device)
+        self.target_model.load_state_dict(self.model.state_dict())
+        self.target_model.eval()
+
+        self.optimizer = optim.Adam(self.model.parameters(), lr=0.001)
+        self.loss_fn = nn.MSELoss()
+
+        self.replay_buffer = ReplayBuffer(capacity=10000)
+        self.batch_size = 32
+        self.gamma = 0.99
+        self.epsilon = 1.0
+        self.epsilon_decay = 0.999
+        self.epsilon_min = 0.01
+        self.update_target_interval = 100
+
+    def select_action(self, state):
+        if np.random.rand() <= self.epsilon:
+            return np.random.choice(self.action_size)
+        state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
+        q_values = self.model(state)
+        action = q_values.argmax().item()
+        return action
+
+    def train(self):
+        if len(self.replay_buffer) < self.batch_size:
+            return
+
+        states, actions, rewards, next_states, dones = self.replay_buffer.sample(self.batch_size)
+
+        states = torch.FloatTensor(states).to(self.device)
+        actions = torch.LongTensor(actions).unsqueeze(1).to(self.device)
+        rewards = torch.FloatTensor(rewards).unsqueeze(1).to(self.device)
+        next_states = torch.FloatTensor(next_states).to(self.device)
+        dones = torch.FloatTensor(dones).unsqueeze(1).to(self.device)
+
+        q_values = self.model(states).gather(1, actions)
+        next_q_values = self.target_model(next_states).max(1)[0].unsqueeze(1)
+        target_q_values = rewards + (1 - dones) * self.gamma * next_q_values
+
+        loss = self.loss_fn(q_values, target_q_values.detach())
+
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+
+    def update_target_model(self):
+        self.target_model.load_state_dict(self.model.state_dict())
+
+    def update_epsilon(self):
+        self.epsilon = max(self.epsilon * self.epsilon_decay, self.epsilon_min)
+
+# 创建DQN Agent
+env = gym.make('SuperMarioBros-1-1-v0')
+state_size = env.observation_space.shape[0]
+action_size = env.action_space.n
+agent = DQNAgent(state_size, action_size)
+
+# 训练DQN Agent
+num_episodes = 1000
+scores = []
+for episode in range(num_episodes):
+    state = env.reset()
+    score = 0
+    done = False
+    while not done:
+        action = agent.select_action(state)
+        next_state, reward, done, _ = env.step(action)
+        agent.replay_buffer.push(state, action, reward, next_state, done)
+        state = next_state
+        score += reward
+        agent.train()
+        if episode % agent.update_target_interval == 0:
+            agent.update_target_model()
+        agent.update_epsilon()
+    scores.append(score)
+    if episode % 100 == 0:
+        print(f"Episode: {episode}, Average Score: {np.mean(scores[-100:])}")
+
+# 绘制训练过程中的分数曲线
+plt.plot(scores)
+plt.xlabel('Episode')
+plt.ylabel('Score')
+plt.title('Training Progress')
+plt.show()
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