今日大事件新闻免费的seo优化
视频讲解：
DQN 玩 2048 实战｜第三期！优化网络，使用GPU、Env奖励优化
1. 仅考虑局部合并奖励：目前的奖励只设置为合并方块时获得的分数，只关注了每一步的即时合并收益，而没有对最终达成 2048 这个目标给予额外的激励，如果没有对达成 2048 给予足够的奖励信号，Agent 可能不会将其作为一个重要的目标
2. 训练硬件资源利用不高，没有使用GPU进行加速，默认为CPU，较慢
代码修改如下：
step函数里面，输入维度增加max_tile最大的数是多少
if 2048 in self.board:reward += 10000done = True
state = self.board.flatten()
max_tile = np.max(self.board)
state = np.append(state, max_tile)
return state, reward, doneinput_size = 17检查系统中是否有可用的 GPU，如果有则使用 GPU 进行计算，否则使用 CPU。
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")在 train ，创建模型实例后，使用 .to(device) 将模型移动到指定的设备（GPU 或 CPU）
model = DQN(input_size, output_size).to(device)
target_model = DQN(input_size, output_size).to(device)在训练和推理过程中，将输入数据（状态、动作、奖励等）也移动到指定的设备上。
state = torch.FloatTensor(state).unsqueeze(0).to(device)next_state = torch.FloatTensor(next_state).unsqueeze(0).to(device)states = torch.FloatTensor(states).to(device)
actions = torch.LongTensor(actions).to(device)
rewards = torch.FloatTensor(rewards).to(device)
next_states = torch.FloatTensor(next_states).to(device)
dones = torch.FloatTensor(dones).to(device)将 state 和 next_state 先使用 .cpu() 方法移动到 CPU 上，再使用 .numpy() 方法转换为 NumPy 数组
replay_buffer.add(state.cpu().squeeze(0).numpy(), action, reward, next_state.cpu().squeeze(0).numpy(), done)这个不改的话，会出现 TypeError: can't convert cuda:0 device type tensor to numpy 错误
完整代码如下：
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import random
from collections import deque
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.table import Tabledevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")# 2048 游戏环境类
class Game2048:def __init__(self):self.board = np.zeros((4, 4), dtype=int)self.add_random_tile()self.add_random_tile()def add_random_tile(self):empty_cells = np.argwhere(self.board == 0)if len(empty_cells) > 0:index = random.choice(empty_cells)self.board[index[0], index[1]] = 2 if random.random() < 0.9 else 4def move_left(self):reward = 0new_board = np.copy(self.board)for row in range(4):line = new_board[row]non_zero = line[line != 0]merged = []i = 0while i < len(non_zero):if i + 1 < len(non_zero) and non_zero[i] == non_zero[i + 1]:merged.append(2 * non_zero[i])reward += 2 * non_zero[i]i += 2else:merged.append(non_zero[i])i += 1new_board[row] = np.pad(merged, (0, 4 - len(merged)), 'constant')if not np.array_equal(new_board, self.board):self.board = new_boardself.add_random_tile()return rewarddef move_right(self):self.board = np.fliplr(self.board)reward = self.move_left()self.board = np.fliplr(self.board)return rewarddef move_up(self):self.board = self.board.Treward = self.move_left()self.board = self.board.Treturn rewarddef move_down(self):self.board = self.board.Treward = self.move_right()self.board = self.board.Treturn rewarddef step(self, action):if action == 0:reward = self.move_left()elif action == 1:reward = self.move_right()elif action == 2:reward = self.move_up()elif action == 3:reward = self.move_down()done = not np.any(self.board == 0) and all([np.all(self.board[:, i] != self.board[:, i + 1]) for i in range(3)]) and all([np.all(self.board[i, :] != self.board[i + 1, :]) for i in range(3)])if 2048 in self.board:reward += 10000done = Truestate = self.board.flatten()max_tile = np.max(self.board)state = np.append(state, max_tile)return state, reward, donedef reset(self):self.board = np.zeros((4, 4), dtype=int)self.add_random_tile()self.add_random_tile()state = self.board.flatten()max_tile = np.max(self.board)state = np.append(state, max_tile)return state# 深度 Q 网络类
class DQN(nn.Module):def __init__(self, input_size, output_size):super(DQN, self).__init__()self.fc1 = nn.Linear(input_size, 128)self.fc2 = nn.Linear(128, 128)self.fc3 = nn.Linear(128, output_size)def forward(self, x):x = torch.relu(self.fc1(x))x = torch.relu(self.fc2(x))return self.fc3(x)# 经验回放缓冲区类
class ReplayBuffer:def __init__(self, capacity):self.buffer = deque(maxlen=capacity)def add(self, state, action, reward, next_state, done):self.buffer.append((state, action, reward, next_state, done))def sample(self, batch_size):batch = random.sample(self.buffer, batch_size)states, actions, rewards, next_states, dones = zip(*batch)return np.array(states), np.array(actions), np.array(rewards), np.array(next_states), np.array(dones)def __len__(self):return len(self.buffer)# 可视化函数
def visualize_board(board, ax):ax.clear()table = Table(ax, bbox=[0, 0, 1, 1])nrows, ncols = board.shapewidth, height = 1.0 / ncols, 1.0 / nrows# 定义颜色映射cmap = mcolors.LinearSegmentedColormap.from_list("", ["white", "yellow", "orange", "red"])for (i, j), val in np.ndenumerate(board):color = cmap(np.log2(val + 1) / np.log2(2048 + 1)) if val > 0 else "white"table.add_cell(i, j, width, height, text=val if val > 0 else "",loc='center', facecolor=color)ax.add_table(table)ax.set_axis_off()plt.draw()plt.pause(0.1)# 训练函数
def train():env = Game2048()input_size = 17output_size = 4model = DQN(input_size, output_size).to(device)target_model = DQN(input_size, output_size).to(device)target_model.load_state_dict(model.state_dict())target_model.eval()optimizer = optim.Adam(model.parameters(), lr=0.001)criterion = nn.MSELoss()replay_buffer = ReplayBuffer(capacity=10000)batch_size = 32gamma = 0.99epsilon = 1.0epsilon_decay = 0.995epsilon_min = 0.01update_target_freq = 10num_episodes = 1000fig, ax = plt.subplots()for episode in range(num_episodes):state = env.reset()state = torch.FloatTensor(state).unsqueeze(0).to(device)done = Falsetotal_reward = 0while not done:visualize_board(env.board, ax)if random.random() < epsilon:action = random.randint(0, output_size - 1)else:q_values = model(state)action = torch.argmax(q_values, dim=1).item()next_state, reward, done = env.step(action)next_state = torch.FloatTensor(next_state).unsqueeze(0).to(device)replay_buffer.add(state.cpu().squeeze(0).numpy(), action, reward, next_state.cpu().squeeze(0).numpy(), done)if len(replay_buffer) >= batch_size:states, actions, rewards, next_states, dones = replay_buffer.sample(batch_size)states = torch.FloatTensor(states).to(device)actions = torch.LongTensor(actions).to(device)rewards = torch.FloatTensor(rewards).to(device)next_states = torch.FloatTensor(next_states).to(device)dones = torch.FloatTensor(dones).to(device)q_values = model(states)q_values = q_values.gather(1, actions.unsqueeze(1)).squeeze(1)next_q_values = target_model(next_states)next_q_values = next_q_values.max(1)[0]target_q_values = rewards + gamma * (1 - dones) * next_q_valuesloss = criterion(q_values, target_q_values)optimizer.zero_grad()loss.backward()optimizer.step()state = next_statetotal_reward += rewardif episode % update_target_freq == 0:target_model.load_state_dict(model.state_dict())epsilon = max(epsilon * epsilon_decay, epsilon_min)print(f"Episode {episode}: Total Reward = {total_reward}, Epsilon = {epsilon}")plt.close()if __name__ == "__main__":train()