{"id":1036592,"date":"2024-12-31T12:08:13","date_gmt":"2024-12-31T04:08:13","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1036592.html"},"modified":"2024-12-31T12:08:15","modified_gmt":"2024-12-31T04:08:15","slug":"python%e6%b7%b1%e5%ba%a6%e5%bc%ba%e5%8c%96%e5%ad%a6%e4%b9%a0%e5%a6%82%e4%bd%95%e6%b1%82%e5%87%ba%e6%9b%b2%e7%ba%bf","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1036592.html","title":{"rendered":"python\u6df1\u5ea6\u5f3a\u5316\u5b66\u4e60\u5982\u4f55\u6c42\u51fa\u66f2\u7ebf"},"content":{"rendered":"

\"python\u6df1\u5ea6\u5f3a\u5316\u5b66\u4e60\u5982\u4f55\u6c42\u51fa\u66f2\u7ebf\"<\/p>\n

Python\u6df1\u5ea6\u5f3a\u5316\u5b66\u4e60\u5982\u4f55\u6c42\u51fa\u66f2\u7ebf<\/strong>\uff1a<\/p>\n<\/p>\n

Python\u6df1\u5ea6\u5f3a\u5316\u5b66\u4e60\u6c42\u51fa\u66f2\u7ebf\u7684\u65b9\u6cd5\u4e3b\u8981\u6709\uff1a\u4f7f\u7528Gym\u73af\u5883\u8fdb\u884c\u6a21\u62df\u8bad\u7ec3\u3001\u5e94\u7528\u6df1\u5ea6Q\u7f51\u7edc\uff08DQN\uff09\u7b97\u6cd5\u3001\u5229\u7528\u7b56\u7565\u68af\u5ea6\u65b9\u6cd5\u3001\u7ed3\u5408TensorFlow\u6216PyTorch\u7b49\u6846\u67b6\u3001\u901a\u8fc7Matplotlib\u7b49\u5de5\u5177\u8fdb\u884c\u53ef\u89c6\u5316\u3002\u4f7f\u7528Gym\u73af\u5883\u8fdb\u884c\u6a21\u62df\u8bad\u7ec3<\/strong>\u662f\u6c42\u51fa\u66f2\u7ebf\u7684\u4e00\u4e2a\u91cd\u8981\u6b65\u9aa4\u3002Gym\u73af\u5883\u662f\u4e00\u4e2a\u7528\u4e8e\u5f00\u53d1\u548c\u6bd4\u8f83\u5f3a\u5316\u5b66\u4e60\u7b97\u6cd5\u7684\u5de5\u5177\u5305\uff0c\u5b83\u63d0\u4f9b\u4e86\u5404\u79cd\u6a21\u62df\u73af\u5883\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u8fdb\u884c\u5404\u79cd\u5f3a\u5316\u5b66\u4e60\u4efb\u52a1\u7684\u6a21\u62df\u548c\u8bad\u7ec3\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Gym\u73af\u5883\u8fdb\u884c\u6a21\u62df\u8bad\u7ec3<\/p>\n<\/p>\n

Gym\u73af\u5883\u662fOpenAI<\/a>\u63a8\u51fa\u7684\u4e00\u4e2a\u5de5\u5177\u5305\uff0c\u7528\u4e8e\u5f00\u53d1\u548c\u6bd4\u8f83\u5f3a\u5316\u5b66\u4e60\u7b97\u6cd5\u3002\u5b83\u63d0\u4f9b\u4e86\u8bb8\u591a\u6a21\u62df\u73af\u5883\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u8fdb\u884c\u5404\u79cd\u5f3a\u5316\u5b66\u4e60\u4efb\u52a1\u7684\u6a21\u62df\u548c\u8bad\u7ec3\u3002\u901a\u8fc7\u4f7f\u7528Gym\u73af\u5883\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u5b9a\u4e49\u5f3a\u5316\u5b66\u4e60\u4efb\u52a1\uff0c\u5e76\u8fdb\u884c\u8bad\u7ec3\uff0c\u6700\u7ec8\u6c42\u51fa\u76f8\u5173\u7684\u66f2\u7ebf\u3002<\/p>\n<\/p>\n

\u5728\u4f7f\u7528Gym\u73af\u5883\u8fdb\u884c\u6a21\u62df\u8bad\u7ec3\u65f6\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5Gym\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install gym<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5bfc\u5165Gym\u5e93\uff0c\u5e76\u9009\u62e9\u4e00\u4e2a\u73af\u5883\u8fdb\u884c\u8bad\u7ec3\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u9009\u62e9CartPole-v1\u73af\u5883\uff1a<\/p>\n<\/p>\n
import gym<\/p>\n
env = gym.make('CartPole-v1')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u53ef\u4ee5\u521d\u59cb\u5316\u73af\u5883\uff0c\u5e76\u5f00\u59cb\u8bad\u7ec3\u4ee3\u7406\u3002\u4ee3\u7406\u5728\u73af\u5883\u4e2d\u8fdb\u884c\u4e00\u7cfb\u5217\u7684\u52a8\u4f5c\uff0c\u5e76\u6839\u636e\u73af\u5883\u7684\u53cd\u9988\u8fdb\u884c\u5b66\u4e60\u3002\u901a\u8fc7\u591a\u6b21\u8bad\u7ec3\uff0c\u6211\u4eec\u53ef\u4ee5\u83b7\u5f97\u4ee3\u7406\u5728\u4e0d\u540c\u72b6\u6001\u4e0b\u7684\u8868\u73b0\uff0c\u5e76\u7ed8\u5236\u51fa\u76f8\u5173\u7684\u66f2\u7ebf\u3002<\/p>\n<\/p>\n
observation = env.reset()<\/p>\n
for _ in range(1000):<\/p>\n
    env.render()<\/p>\n
    action = env.action_space.sample()<\/p>\n
    observation, reward, done, info = env.step(action)<\/p>\n
    if done:<\/p>\n
        observation = env.reset()<\/p>\n
env.close()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u8fc7\u7a0b\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u8bb0\u5f55\u4ee3\u7406\u7684\u8868\u73b0\uff0c\u5e76\u4f7f\u7528Matplotlib\u7b49\u5de5\u5177\u7ed8\u5236\u51fa\u76f8\u5173\u7684\u66f2\u7ebf\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u5e94\u7528\u6df1\u5ea6Q\u7f51\u7edc\uff08DQN\uff09\u7b97\u6cd5<\/p>\n<\/p>\n
\u6df1\u5ea6Q\u7f51\u7edc\uff08DQN\uff09\u7b97\u6cd5\u662f\u4e00\u79cd\u5e38\u7528\u7684\u6df1\u5ea6\u5f3a\u5316\u5b66\u4e60\u7b97\u6cd5\u3002\u5b83\u7ed3\u5408\u4e86Q\u5b66\u4e60\u548c\u795e\u7ecf\u7f51\u7edc\uff0c\u901a\u8fc7\u795e\u7ecf\u7f51\u7edc\u6765\u903c\u8fd1Q\u503c\u51fd\u6570\uff0c\u4ece\u800c\u5b9e\u73b0\u5f3a\u5316\u5b66\u4e60\u4efb\u52a1\u7684\u8bad\u7ec3\u3002\u4f7f\u7528DQN\u7b97\u6cd5\u53ef\u4ee5\u6709\u6548\u5730\u6c42\u51fa\u66f2\u7ebf\u3002<\/p>\n<\/p>\n
\u5728\u4f7f\u7528DQN\u7b97\u6cd5\u65f6\uff0c\u9996\u5148\u9700\u8981\u5b9a\u4e49\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u3002\u53ef\u4ee5\u4f7f\u7528TensorFlow\u6216PyTorch\u7b49\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u6765\u5b9a\u4e49\u6a21\u578b\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684DQN\u6a21\u578b\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import torch<\/p>\n
import torch.nn as nn<\/p>\n
import torch.optim as optim<\/p>\n
class DQN(nn.Module):<\/p>\n
    def __init__(self, input_dim, output_dim):<\/p>\n
        super(DQN, self).__init__()<\/p>\n
        self.fc1 = nn.Linear(input_dim, 128)<\/p>\n
        self.fc2 = nn.Linear(128, 128)<\/p>\n
        self.fc3 = nn.Linear(128, output_dim)<\/p>\n
    def forward(self, x):<\/p>\n
        x = torch.relu(self.fc1(x))<\/p>\n
        x = torch.relu(self.fc2(x))<\/p>\n
        x = self.fc3(x)<\/p>\n
        return x<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b9a\u4e49\u597d\u6a21\u578b\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u6784\u5efaDQN\u7b97\u6cd5\uff0c\u5e76\u8fdb\u884c\u8bad\u7ec3\u3002\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u8bb0\u5f55\u4ee3\u7406\u7684\u8868\u73b0\uff0c\u5e76\u7ed8\u5236\u51fa\u76f8\u5173\u7684\u66f2\u7ebf\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u5229\u7528\u7b56\u7565\u68af\u5ea6\u65b9\u6cd5<\/p>\n<\/p>\n
\u7b56\u7565\u68af\u5ea6\u65b9\u6cd5\u662f\u4e00\u7c7b\u5e38\u7528\u7684\u5f3a\u5316\u5b66\u4e60\u7b97\u6cd5\uff0c\u901a\u8fc7\u4f18\u5316\u7b56\u7565\u51fd\u6570\u6765\u5b9e\u73b0\u5f3a\u5316\u5b66\u4e60\u4efb\u52a1\u7684\u8bad\u7ec3\u3002\u5e38\u89c1\u7684\u7b56\u7565\u68af\u5ea6\u65b9\u6cd5\u5305\u62ecREINFORCE\u7b97\u6cd5\u3001Actor-Critic\u7b97\u6cd5\u7b49\u3002\u5229\u7528\u7b56\u7565\u68af\u5ea6\u65b9\u6cd5\u53ef\u4ee5\u6709\u6548\u5730\u6c42\u51fa\u66f2\u7ebf\u3002<\/p>\n<\/p>\n
\u5728\u4f7f\u7528\u7b56\u7565\u68af\u5ea6\u65b9\u6cd5\u65f6\uff0c\u9996\u5148\u9700\u8981\u5b9a\u4e49\u7b56\u7565\u7f51\u7edc\u6a21\u578b\u3002\u53ef\u4ee5\u4f7f\u7528TensorFlow\u6216PyTorch\u7b49\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u6765\u5b9a\u4e49\u6a21\u578b\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u7b56\u7565\u7f51\u7edc\u6a21\u578b\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import torch<\/p>\n
import torch.nn as nn<\/p>\n
import torch.optim as optim<\/p>\n
class PolicyNetwork(nn.Module):<\/p>\n
    def __init__(self, input_dim, output_dim):<\/p>\n
        super(PolicyNetwork, self).__init__()<\/p>\n
        self.fc1 = nn.Linear(input_dim, 128)<\/p>\n
        self.fc2 = nn.Linear(128, 128)<\/p>\n
        self.fc3 = nn.Linear(128, output_dim)<\/p>\n
    def forward(self, x):<\/p>\n
        x = torch.relu(self.fc1(x))<\/p>\n
        x = torch.relu(self.fc2(x))<\/p>\n
        x = torch.softmax(self.fc3(x), dim=-1)<\/p>\n
        return x<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b9a\u4e49\u597d\u7b56\u7565\u7f51\u7edc\u6a21\u578b\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u6784\u5efa\u7b56\u7565\u68af\u5ea6\u7b97\u6cd5\uff0c\u5e76\u8fdb\u884c\u8bad\u7ec3\u3002\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u8bb0\u5f55\u4ee3\u7406\u7684\u8868\u73b0\uff0c\u5e76\u7ed8\u5236\u51fa\u76f8\u5173\u7684\u66f2\u7ebf\u3002<\/p>\n<\/p>\n
\u56db\u3001\u7ed3\u5408TensorFlow\u6216PyTorch\u7b49\u6846\u67b6<\/p>\n<\/p>\n
TensorFlow\u548cPyTorch\u662f\u4e24\u79cd\u5e38\u7528\u7684\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u6df1\u5ea6\u5f3a\u5316\u5b66\u4e60\u4efb\u52a1\u4e2d\u3002\u901a\u8fc7\u7ed3\u5408\u8fd9\u4e9b\u6846\u67b6\uff0c\u6211\u4eec\u53ef\u4ee5\u65b9\u4fbf\u5730\u5b9a\u4e49\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\uff0c\u6784\u5efa\u5f3a\u5316\u5b66\u4e60\u7b97\u6cd5\uff0c\u5e76\u8fdb\u884c\u8bad\u7ec3\uff0c\u4ece\u800c\u6c42\u51fa\u76f8\u5173\u7684\u66f2\u7ebf\u3002<\/p>\n<\/p>\n
\u5728\u4f7f\u7528TensorFlow\u6216PyTorch\u8fdb\u884c\u5f3a\u5316\u5b66\u4e60\u4efb\u52a1\u65f6\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u76f8\u5e94\u7684\u6846\u67b6\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install tensorflow<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6216<\/p>\n<\/p>\n
pip install torch<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5b9a\u4e49\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\uff0c\u6784\u5efa\u5f3a\u5316\u5b66\u4e60\u7b97\u6cd5\uff0c\u5e76\u8fdb\u884c\u8bad\u7ec3\u3002\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u8bb0\u5f55\u4ee3\u7406\u7684\u8868\u73b0\uff0c\u5e76\u4f7f\u7528Matplotlib\u7b49\u5de5\u5177\u7ed8\u5236\u51fa\u76f8\u5173\u7684\u66f2\u7ebf\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u901a\u8fc7Matplotlib\u7b49\u5de5\u5177\u8fdb\u884c\u53ef\u89c6\u5316<\/p>\n<\/p>\n
\u5728\u8fdb\u884c\u6df1\u5ea6\u5f3a\u5316\u5b66\u4e60\u4efb\u52a1\u7684\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0c\u6211\u4eec\u901a\u5e38\u9700\u8981\u5bf9\u4ee3\u7406\u7684\u8868\u73b0\u8fdb\u884c\u53ef\u89c6\u5316\uff0c\u4ee5\u4fbf\u89c2\u5bdf\u548c\u5206\u6790\u8bad\u7ec3\u8fc7\u7a0b\u3002Matplotlib\u662f\u4e00\u4e2a\u5e38\u7528\u7684\u53ef\u89c6\u5316\u5de5\u5177\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u7ed8\u5236\u51fa\u5404\u79cd\u7c7b\u578b\u7684\u66f2\u7ebf\u3002<\/p>\n<\/p>\n
\u5728\u4f7f\u7528Matplotlib\u8fdb\u884c\u53ef\u89c6\u5316\u65f6\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5Matplotlib\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5bfc\u5165Matplotlib\u5e93\uff0c\u5e76\u7ed8\u5236\u51fa\u76f8\u5173\u7684\u66f2\u7ebf\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e9b\u8bad\u7ec3\u6570\u636e<\/strong><\/h2>\n
episodes = list(range(1, 101))<\/p>\n
rewards = [i0.5 for i in episodes]<\/p>\n
plt.plot(episodes, rewards)<\/p>\n
plt.xlabel('Episode')<\/p>\n
plt.ylabel('Reward')<\/p>\n
plt.title('Training Performance')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u65b9\u4fbf\u5730\u5bf9\u6df1\u5ea6\u5f3a\u5316\u5b66\u4e60\u4efb\u52a1\u7684\u8bad\u7ec3\u8fc7\u7a0b\u8fdb\u884c\u53ef\u89c6\u5316\uff0c\u5e76\u5206\u6790\u4ee3\u7406\u7684\u8868\u73b0\uff0c\u4ece\u800c\u6c42\u51fa\u76f8\u5173\u7684\u66f2\u7ebf\u3002<\/p>\n<\/p>\n
\u603b\u7ed3<\/p>\n<\/p>\n
\u5728Python\u6df1\u5ea6\u5f3a\u5316\u5b66\u4e60\u4e2d\uff0c\u6c42\u51fa\u66f2\u7ebf\u7684\u65b9\u6cd5\u4e3b\u8981\u6709\uff1a\u4f7f\u7528Gym\u73af\u5883\u8fdb\u884c\u6a21\u62df\u8bad\u7ec3\u3001\u5e94\u7528\u6df1\u5ea6Q\u7f51\u7edc\uff08DQN\uff09\u7b97\u6cd5\u3001\u5229\u7528\u7b56\u7565\u68af\u5ea6\u65b9\u6cd5\u3001\u7ed3\u5408TensorFlow\u6216PyTorch\u7b49\u6846\u67b6\u3001\u901a\u8fc7Matplotlib\u7b49\u5de5\u5177\u8fdb\u884c\u53ef\u89c6\u5316\u3002\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u5f3a\u5316\u5b66\u4e60\u4efb\u52a1\u7684\u8bad\u7ec3\u548c\u5206\u6790\uff0c\u6700\u7ec8\u6c42\u51fa\u76f8\u5173\u7684\u66f2\u7ebf\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
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