Spring Bean的初始化和销毁方法二:让bean实现InitializingBean和DisposableBean接口

本文通过一个具体的示例展示了如何使用 Spring 框架管理 Bean 的生命周期,包括 Bean 的初始化和销毁过程。示例中定义了一个 Car 实体类,实现了 InitializingBean 和 DisposableBean 接口以自定义初始化和销毁方法,并通过配置类和测试类演示了整个生命周期。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

1、pom.xml

<dependencies>
    <dependency>
        <groupId>org.springframework</groupId>
        <artifactId>spring-context</artifactId>
        <version>5.0.6.RELEASE</version>
    </dependency>
    <dependency>
        <groupId>org.projectlombok</groupId>
        <artifactId>lombok</artifactId>
        <version>1.18.2</version>
    </dependency>
</dependencies>

2、实体类

import org.springframework.beans.factory.DisposableBean;
import org.springframework.beans.factory.InitializingBean;

public class Car implements InitializingBean, DisposableBean {
    public Car () {
        System.out.println("car constructor...");
    }
    @Override
    public void destroy() throws Exception {
        System.out.println("car destory...");
    }
    @Override
    public void afterPropertiesSet() throws Exception {
        System.out.println("car afterPropertiesSet...");
    }
}


3、配置类
import com.it.huaxin.vo.Car;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@Configuration
public class LifeCycleConfig {
    @Bean
    public Car car() {
        return new Car();
    }
}

4、测试类

import com.it.huaxin.config.LifeCycleConfig;
import org.springframework.context.annotation.AnnotationConfigApplicationContext;

public class LifeCycleTest {
    public static void main(String [] args) {
        AnnotationConfigApplicationContext context = new AnnotationConfigApplicationContext(LifeCycleConfig.class);
        context.close();
    }
}

//结果:

car constructor...
car afterPropertiesSet...
car destory...

DQN(Deep Q-Network)是一种使用深度神经网络实现的强化学习算法,用于解决离散动作空间的问题。在PyTorch中实现DQN可以分为以下几个步骤: 1. 定义神经网络:使用PyTorch定义一个包含多个全连接层的神经网络,输入为状态空间的维度,输出为动作空间的维度。 ```python import torch.nn as nn import torch.nn.functional as F class QNet(nn.Module): def __init__(self, state_dim, action_dim): super(QNet, self).__init__() self.fc1 = nn.Linear(state_dim, 64) self.fc2 = nn.Linear(64, 64) self.fc3 = nn.Linear(64, action_dim) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x ``` 2. 定义经验回放缓存:包含多条经验,每条经验包含一个状态、一个动作、一个奖励和下一个状态。 ```python import random class ReplayBuffer(object): def __init__(self, max_size): self.buffer = [] self.max_size = max_size def push(self, state, action, reward, next_state): if len(self.buffer) < self.max_size: self.buffer.append((state, action, reward, next_state)) else: self.buffer.pop(0) self.buffer.append((state, action, reward, next_state)) def sample(self, batch_size): state, action, reward, next_state = zip(*random.sample(self.buffer, batch_size)) return torch.stack(state), torch.tensor(action), torch.tensor(reward), torch.stack(next_state) ``` 3. 定义DQN算法:使用PyTorch定义DQN算法,包含训练和预测两个方法。 ```python class DQN(object): def __init__(self, state_dim, action_dim, gamma, epsilon, lr): self.qnet = QNet(state_dim, action_dim) self.target_qnet = QNet(state_dim, action_dim) self.gamma = gamma self.epsilon = epsilon self.lr = lr self.optimizer = torch.optim.Adam(self.qnet.parameters(), lr=self.lr) self.buffer = ReplayBuffer(100000) self.loss_fn = nn.MSELoss() def act(self, state): if random.random() < self.epsilon: return random.randint(0, action_dim - 1) else: with torch.no_grad(): q_values = self.qnet(state) return q_values.argmax().item() def train(self, batch_size): state, action, reward, next_state = self.buffer.sample(batch_size) q_values = self.qnet(state).gather(1, action.unsqueeze(1)).squeeze(1) target_q_values = self.target_qnet(next_state).max(1)[0].detach() expected_q_values = reward + self.gamma * target_q_values loss = self.loss_fn(q_values, expected_q_values) self.optimizer.zero_grad() loss.backward() self.optimizer.step() def update_target_qnet(self): self.target_qnet.load_state_dict(self.qnet.state_dict()) ``` 4. 训练模型:使用DQN算法进行训练,并更新目标Q网络。 ```python dqn = DQN(state_dim, action_dim, gamma=0.99, epsilon=1.0, lr=0.001) for episode in range(num_episodes): state = env.reset() total_reward = 0 for step in range(max_steps): action = dqn.act(torch.tensor(state, dtype=torch.float32)) next_state, reward, done, _ = env.step(action) dqn.buffer.push(torch.tensor(state, dtype=torch.float32), action, reward, torch.tensor(next_state, dtype=torch.float32)) state = next_state total_reward += reward if len(dqn.buffer.buffer) > batch_size: dqn.train(batch_size) if step % target_update == 0: dqn.update_target_qnet() if done: break dqn.epsilon = max(0.01, dqn.epsilon * 0.995) ``` 5. 测试模型:使用训练好的模型进行测试。 ```python total_reward = 0 state = env.reset() while True: action = dqn.act(torch.tensor(state, dtype=torch.float32)) next_state, reward, done, _ = env.step(action) state = next_state total_reward += reward if done: break print("Total reward: {}".format(total_reward)) ``` 以上就是在PyTorch中实现DQN强化学习的基本步骤。需要注意的是,DQN算法中还有很多细节和超参数需要调整,具体实现过程需要根据具体问题进行调整。
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包

打赏作者

青春1314

你的鼓励将是我创作的最大动力

¥1 ¥2 ¥4 ¥6 ¥10 ¥20
扫码支付:¥1
获取中
扫码支付

您的余额不足,请更换扫码支付或充值

打赏作者

实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值