User: Command 'mkfontscale' not found, but can be installed with:
sudo apt install xfonts-utils
Assistant: The error message you're seeing indicates that the `mkfontscale` command is missing from your system. This command is part of the `xfonts-utils` package on Debian-based systems like Ubuntu. To resolve this, you can install the package using the following steps:

1. **Open Terminal**: Press `Ctrl` + `Alt` + `T` to open a terminal window.

2. **Update Package List**: Before installing a new package, it's a good practice to update the package list to ensure you get the latest version. Run the command:
   ```
   sudo apt update
   ```

3. **Install xfonts-utils**: Now, install the `xfonts-utils` package by running:
   ```
   sudo apt install xfonts-utils
   ```

4. **Verify Installation**: After installation, you can verify that `mkfontscale` is installed correctly by running:
   ```
   mkfontscale --version
   ```
   This should output version information for `mkfontscale`, indicating that it has been installed successfully.

These steps will install the required package, and you should be able to use the `mkfontscale` command without any issues.
User:  xfonts-utils : Depends: libfontenc1 (>= 1:1.1.4) but it is not going to be installed
                Depends: xfonts-encodings but it is not going to be installed
E: Unmet dependencies. Try 'apt --fix-broken install' with no packages (or specify a solution).
Assistant: When you encounter unmet dependencies while trying to install a package, it typically means that additional packages are required but cannot be installed due to conflicts or other issues. Here's how you can address this:

1. **Fix Broken Dependencies**: Run the following command to attempt to fix any broken dependencies on your system:
   ```bash
   sudo apt --fix-broken install
   ```
   This command will try to automatically correct any issues with dependencies.

2. **Update and Upgrade**: Make sure your package list is up-to-date, and upgrade any outdated packages. Run:
   ```bash
   sudo apt update
   sudo apt upgrade
   ```

3. **Retry Installation**: After fixing broken dependencies and upgrading packages, try installing `xfonts-utils` again:
   ```bash
   sudo apt install xfonts-utils
   ```

4. **Install Manually (if necessary)**: If the above steps don't resolve the issue, you can attempt to manually install the specific dependencies. First, try installing the missing dependencies explicitly:
   ```bash
   sudo apt install libfontenc1 xfonts-encodings
   ```
   Then, try installing `xfonts-utils` again.

5. **Check for Held Packages**: Sometimes, packages are held back from being upgraded. Check for held packages with:
   ```bash
   sudo apt-mark showhold
   ```
   If any packages are listed, you can unhold them with:
   ```bash
   sudo apt-mark unhold [package_name]
   ```

These steps should help you resolve the unmet dependencies issue and allow you to install the required packages successfully. If you continue to experience issues, please let me know!
User: 中文回答
Assistant: 当你在安装软件包时遇到未满足的依赖关系错误，这是因为系统中缺少一些必要的软件包。你可以按照以下步骤来解决这个问题：

1. **修复破损的依赖关系**：使用以下命令尝试修复系统中任何破损的依赖关系：
   ```bash
   sudo apt --fix-broken install
   ```
   这个命令会尝试自动修正任何依赖关系问题。

2. **更新和升级软件包**：确保你的软件包列表是最新的，并升级任何过时的软件包。运行：
   ```bash
   sudo apt update
   sudo apt upgrade
   ```

3. **重新尝试安装**：在修复破损的依赖关系并升级软件包之后，再次尝试安装 `xfonts-utils`：
   ```bash
   sudo apt install xfonts-utils
   ```

4. **手动安装（如有必要）**：如果上面的步骤没有解决问题，可以尝试手动安装缺少的依赖项。首先，尝试显式安装缺失的依赖包：
   ```bash
   sudo apt install libfontenc1 xfonts-encodings
   ```
   然后，再次尝试安装 `xfonts-utils`。

5. **检查被保留的软件包**：有时，软件包可能被保留而没有升级。检查被保留的软件包：
   ```bash
   sudo apt-mark showhold
   ```
   如果有软件包被列出，你可以用以下命令取消保留：
   ```bash
   sudo apt-mark unhold [package_name]
   ```

这些步骤应该能帮助你解决未满足的依赖关系问题，并成功安装所需的软件包。如果你还有其他问题，请随时告诉我！
User:  chmod什么命令
Assistant: `chmod` 是 Linux 和 UNIX 系统中用于更改文件或目录权限的命令。权限决定了谁可以读取、写入或执行文件。权限由三部分组成：用户（Owner）、组（Group）和其他人（Others）。每个部分都有三种权限：读（Read）、写（Write）、执行（Execute）。

### 常用的 `chmod` 命令格式：

1. **符号模式（Symbolic Mode）**：

   使用符号（如 `u`、`g`、`o` 和 `a`）来表示权限变更。

   ```bash
   chmod [选项] [u|g|o|a][+|-|=][r|w|x] 文件/目录
   ```

   - `u`: 用户权限
   - `g`: 组权限
   - `o`: 其他用户权限
   - `a`: 全部用户权限（包括用户、组和其他人）
   - `+`: 添加权限
   - `-`: 移除权限
   - `=`: 设置确切权限
   - `r`: 读权限
   - `w`: 写权限
   - `x`: 执行权限

   **示例**：
   ```bash
   chmod u+x myscript.sh     # 给脚本的拥有者添加执行权限
   chmod g-w myfile.txt      # 移除组对文件的写权限
   chmod o=r myfile.txt      # 设置其他人只有读权限
   ```

2. **八进制模式（Numeric Mode）**：

   使用数字来表示权限，常见的数字有：

   - `4` 代表读权限（`r`）
   - `2` 代表写权限（`w`）
   - `1` 代表执行权限（`x`）
   - 这些数字可以相加来复合权限

   八进制模式分为三位，分别对应用户、组和其他人。

   **示例**：
   ```bash
   chmod 755 myscript.sh     # 用户拥有读、写、执行权限，组和其他人只有读和执行权限
   chmod 644 myfile.txt      # 用户拥有读写权限，组和其他人只有读权限
   ```

### 常见的用法：

- 为某个文件添加执行权限给所有用户：
  ```bash
  chmod a+x myscript.sh
  ```

- 将某文件的权限修改为完全开放（危险操作，任何人都可以读写执行）：
  ```bash
  chmod 777 myfile.txt
  ```

使用 `chmod` 前，一定要谨慎考虑权限设置，尤其在涉及敏感文件时，防止不必要的访问或修改。
User: 为什么会生成4个图形窗口
```python
import os
import time 
import gymnasium as gym
import numpy as np
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
import openmc
import matplotlib 
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
plt.rcParams['font.sans-serif']=['SimHei']


 

openmc.config['cross_sections'] = '/home/unknowxxs/python/data/cross_sections.xml'

class NuclearReactorEnv(gym.Env):
    def __init__(self):
        super().__init__()
        self.action_space = gym.spaces.Discrete(11)  # 控制棒位置，0-10
        
        # 观察空间：温度、功率、k_eff、控制棒位置、燃料燃耗
        self.observation_space = gym.spaces.Box(
            low=np.array([500, 0, 0.5, 0, 0]),
            high=np.array([1500, 3000, 1.5, 1, 100]),
            dtype=np.float32
        )
        
        self.max_steps = 1000
        self.current_step = 0
        self._setup_model()
        
    def _setup_model(self):
        # 设置材料
        # UO2燃料
        self.fuel = openmc.Material(name='fuel')
        self.fuel.add_nuclide('U235', 0.05)
        self.fuel.add_nuclide('U238', 0.95)
        self.fuel.add_nuclide('O16', 2.0)
        self.fuel.set_density('g/cm3', 10.0)
        self.fuel.temperature = 293.6  # K

        # 水冷却剂
        self.water = openmc.Material(name='water')
        self.water.add_nuclide('H1', 2)
        self.water.add_nuclide('O16', 1)
        self.water.set_density('g/cm3', 1.0)
        self.water.temperature = 293.6  # K

        # 控制棒 (B4C)
        self.control_rod = openmc.Material(name='control_rod')
        self.control_rod.add_nuclide('B10', 0.8)
        self.control_rod.add_nuclide('B11', 0.2)
        self.control_rod.add_nuclide('C12', 1.0)
        self.control_rod.set_density('g/cm3', 2.52)

        # 创建材料集合
        self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])
        self.materials.export_to_xml()  # 生成 materials.xml

        # 创建几何体
        # 简化的圆柱形反应堆堆芯
        cylinder = openmc.ZCylinder(r=100, boundary_type='reflective')  # 增加了边界条件
        fuel_region = -cylinder
        
        fuel_cell = openmc.Cell(fill=self.fuel, region=fuel_region)
        outside_cell = openmc.Cell(region=+cylinder, fill=self.water)  # 创建外部包围物

        # 创建universe和geometry
        root_universe = openmc.Universe(cells=[fuel_cell, outside_cell])
        self.geometry = openmc.Geometry(root_universe)
        self.geometry.export_to_xml()  # 生成 geometry.xml

        # 设置模拟参数并生成 XML
        self.settings = openmc.Settings()
        self.settings.batches = 100
        self.settings.inactive = 10
        self.settings.particles = 1000
        self.settings.temperature = {'method': 'interpolation'}
        self.settings.export_to_xml()  # 生成 settings.xml
    
    def init_visualization(self):
        """初始化可视化图表"""
        plt.ion()  # 开启交互模式
        self.fig, self.axs = plt.subplots(2, 2, figsize=(15, 10))
        self.fig.suptitle('核反应堆运行状态监测', fontsize=16)
        
        # 初始化数据存储
        self.history = {
            'temperature': [],
            'power': [],
            'k_eff': [],
            'burnup': [],
            'control_rod': [],
            'steps': []
        }
        
        # 配置子图
        self.lines = {}
        
        # 温度和功率图
        self.axs[0, 0].set_title('温度和功率变化')
        self.axs[0, 0].set_xlabel('步数')
        self.lines['temp'], = self.axs[0, 0].plot([], [], 'r-', label='温度 (K)')
        self.lines['power'], = self.axs[0, 0].plot([], [], 'b-', label='功率 (MW)')
        self.axs[0, 0].legend()
        
        # k_eff图
        self.axs[0, 1].set_title('k_eff变化')
        self.axs[0, 1].set_xlabel('步数')
        self.lines['k_eff'], = self.axs[0, 1].plot([], [], 'g-', label='k_eff')
        self.axs[0, 1].axhline(y=1.0, color='r', linestyle='--', label='临界值')
        self.axs[0, 1].legend()
        
        # 控制棒位置图
        self.axs[1, 0].set_title('控制棒位置')
        self.axs[1, 0].set_xlabel('步数')
        self.lines['control'], = self.axs[1, 0].plot([], [], 'k-', label='控制棒位置')
        self.axs[1, 0].legend()
        
        # 燃耗图
        self.axs[1, 1].set_title('燃料燃耗')
        self.axs[1, 1].set_xlabel('步数')
        self.lines['burnup'], = self.axs[1, 1].plot([], [], 'm-', label='燃耗 (MWd/kgU)')
        self.axs[1, 1].legend()
        
        plt.tight_layout()

    def update_visualization(self):
        """更新可视化图表"""
        # 更新历史数据
        self.history['temperature'].append(self.temperature)
        self.history['power'].append(self.power)
        self.history['k_eff'].append(self.k_eff)
        self.history['burnup'].append(self.burnup)
        self.history['control_rod'].append(self.control_rod_pos)
        self.history['steps'].append(self.current_step)
        
        # 更新图表
        steps = self.history['steps']
        
        # 更新温度和功率
        self.lines['temp'].set_data(steps, self.history['temperature'])
        self.lines['power'].set_data(steps, self.history['power'])
        self.axs[0, 0].relim()
        self.axs[0, 0].autoscale_view()
        
        # 更新k_eff
        self.lines['k_eff'].set_data(steps, self.history['k_eff'])
        self.axs[0, 1].relim()
        self.axs[0, 1].autoscale_view()
        
        # 更新控制棒位置
        self.lines['control'].set_data(steps, self.history['control_rod'])
        self.axs[1, 0].relim()
        self.axs[1, 0].autoscale_view()
        
        # 更新燃耗
        self.lines['burnup'].set_data(steps, self.history['burnup'])
        self.axs[1, 1].relim()
        self.axs[1, 1].autoscale_view()
        
        plt.draw()
        plt.pause(0.01)

    def reset(self, seed=None):
        super().reset(seed=seed)
        self.current_step = 0
        self.temperature = 600  # K
        self.power = 1000      # MW
        self.k_eff = 1.0
        self.burnup = 0        # MWd/kgU
        self.control_rod_pos = 0.5
        
        # 初始化可视化
        self.init_visualization()
        
        return self._get_observation(), {}
    
    def _get_observation(self):
        return np.array([
            self.temperature,
            self.power,
            self.k_eff,
            self.control_rod_pos,
            self.burnup
        ], dtype=np.float32)
    
    def step(self, action):
        self.current_step += 1
        
        # 更新控制棒位置
        self.control_rod_pos = action / 10.0
        
        # 运行OpenMC模拟
        try:
            # 更新材料温度
            self.fuel.temperature = self.temperature
            
            # 运行临界计算
            openmc.run()
         
            # 获取k_eff
            with openmc.StatePoint("/home/unknowxxs/python/statepoint.100.h5") as sp:
                self.k_eff = sp.keff.nominal_value
                
            # 更新系统状态
            self._update_state()
            
            # 更新可视化
            self.update_visualization()
            
            # 计算奖励
            reward = self._calculate_reward()
            
            # 检查是否结束
            done = self.current_step >= self.max_steps or self._is_unsafe()
            
        except Exception as e:
            print(f"模拟过程出错: {e}")
            reward = -1000
            done = True
        
        return self._get_observation(), reward, done, False, {}
    
    def _update_state(self):
        # 基于k_eff更新功率
        power_factor = (self.k_eff - 1.0) * 5000
        self.power = np.clip(self.power + power_factor, 0, 3000)
        
        # 更新温度
        delta_temp = (self.power - 1000) * 0.1
        self.temperature = np.clip(self.temperature + delta_temp, 500, 1500)
        
        # 更新燃耗
        self.burnup += self.power * 0.001
        
    def _calculate_reward(self):
        reward = 0
        
        # 奖励稳定的运行状态
        reward -= abs(self.power - 1000) * 0.01  # 目标功率1000MW
        reward -= abs(self.k_eff - 1.0) * 100    # 目标k_eff=1
        reward -= abs(self.temperature - 900) * 0.1  # 目标温度900K
        
        # 惩罚不安全状态
        if self._is_unsafe():
            reward -= 1000
            
        return reward
    
    def _is_unsafe(self):
        return (self.temperature > 1400 or 
                self.power > 2500 or 
                self.k_eff > 1.2)

def train_agent():
    env = make_vec_env(NuclearReactorEnv, n_envs=4)
    
    model = PPO(
        "MlpPolicy",
        env,
        verbose=1,
        learning_rate=0.0003,
        n_steps=2048,
        batch_size=64,
        n_epochs=10
    )
    
    model.learn(total_timesteps=1000)
    model.save("ppo_nuclear_reactor")
    
    return model

def evaluate_agent(model, num_episodes=5):
    env = NuclearReactorEnv()
    
    for episode in range(num_episodes):
        obs, _ = env.reset()
        episode_reward = 0
        done = False
        
        while not done:
            action, _ = model.predict(obs)
            obs, reward, done, _, _ = env.step(action)
            episode_reward += reward
            
            # 打印当前状态
            print(f"步骤 {env.current_step}:")
            print(f"温度: {env.temperature:.1f}K")
            print(f"功率: {env.power:.1f}MW")
            print(f"k_eff: {env.k_eff:.3f}")
            print(f"控制棒位置: {env.control_rod_pos:.2f}")
            print(f"燃耗: {env.burnup:.2f}MWd/kgU")
            print("------------------------")
        
        plt.savefig(f'episode_{episode+1}_results.png')
        print(f"Episode {episode + 1}: 总奖励 = {episode_reward:.2f}\n")
    
    plt.close('all')

model = train_agent()
print("\n评估核反应堆控制性能：")
evaluate_agent(model)

```
Assistant: 如果运行上述代码时，每次都生成了4个图形窗口，这很可能是因为在 `train_agent` 函数中使用了 `make_vec_env(NuclearReactorEnv, n_envs=4)`，这会创建一个包含4个并行环境的向量化环境。在这种设置中，代码同时初始化了4个 `NuclearReactorEnv` 实例，每个实例都会在 `reset` 方法中调用 `init_visualization` 方法。

`init_visualization` 方法中调用了 `plt.subplots(2, 2, figsize=(15, 10))` 创建了一组四个子图，并为每个环境实例都会调用一次。因此，如果你有4个环境实例，它们会分别生成自己的图形窗口，从而导致多个窗口弹出。

### 解决方法

如果你不希望每个并行环境都生成自己的可视化窗口，可以尝试以下修改：

1. **禁用训练时的可视化**：
   可以在训练时禁用可视化，只在评估或需要时进行可视化。如下所示，在 `NuclearReactorEnv` 的 `__init__` 方法中添加一个参数用于控制是否启用可视化：

   ```python
   class NuclearReactorEnv(gym.Env):
       def __init__(self, visualize=False):
           super().__init__()
           self.visualize = visualize
           if self.visualize:
               self.init_visualization()

   # 在创建环境实例时控制可视化
   def train_agent():
       env = make_vec_env(lambda: NuclearReactorEnv(visualize=False), n_envs=4)
       ...
   
   def evaluate_agent(model, num_episodes=5):
       env = NuclearReactorEnv(visualize=True)
       ...
   ```

2. **只是单个环境进行可视化**：
   你可以在训练后，使用单个环境实例进行评估和可视化，如在 `evaluate_agent` 函数中所示。

这种方法确保了在并行训练期间不会创建额外的图形窗口，而只是在需要可视化的评估阶段生成窗口。调整以上代码应有助于解决你在视觉窗口方面的问题。
User: np.clip(self.temperature + delta_temp, 500, 1500)
```python
import os
import time 
import gymnasium as gym
import numpy as np
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
import openmc
import matplotlib 
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
plt.rcParams['font.sans-serif']=['SimHei']


 

openmc.config['cross_sections'] = '/home/unknowxxs/python/data/cross_sections.xml'

class NuclearReactorEnv(gym.Env):
    def __init__(self):
        super().__init__()
        self.action_space = gym.spaces.Discrete(11)  # 控制棒位置，0-10
        
        # 观察空间：温度、功率、k_eff、控制棒位置、燃料燃耗
        self.observation_space = gym.spaces.Box(
            low=np.array([500, 0, 0.5, 0, 0]),
            high=np.array([1500, 3000, 1.5, 1, 100]),
            dtype=np.float32
        )
        
        self.max_steps = 1000
        self.current_step = 0
        self._setup_model()
        
    def _setup_model(self):
        # 设置材料
        # UO2燃料
        self.fuel = openmc.Material(name='fuel')
        self.fuel.add_nuclide('U235', 0.05)
        self.fuel.add_nuclide('U238', 0.95)
        self.fuel.add_nuclide('O16', 2.0)
        self.fuel.set_density('g/cm3', 10.0)
        self.fuel.temperature = 293.6  # K

        # 水冷却剂
        self.water = openmc.Material(name='water')
        self.water.add_nuclide('H1', 2)
        self.water.add_nuclide('O16', 1)
        self.water.set_density('g/cm3', 1.0)
        self.water.temperature = 293.6  # K

        # 控制棒 (B4C)
        self.control_rod = openmc.Material(name='control_rod')
        self.control_rod.add_nuclide('B10', 0.8)
        self.control_rod.add_nuclide('B11', 0.2)
        self.control_rod.add_nuclide('C12', 1.0)
        self.control_rod.set_density('g/cm3', 2.52)

        # 创建材料集合
        self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])
        self.materials.export_to_xml()  # 生成 materials.xml

        # 创建几何体
        # 简化的圆柱形反应堆堆芯
        cylinder = openmc.ZCylinder(r=100, boundary_type='reflective')  # 增加了边界条件
        fuel_region = -cylinder
        
        fuel_cell = openmc.Cell(fill=self.fuel, region=fuel_region)
        outside_cell = openmc.Cell(region=+cylinder, fill=self.water)  # 创建外部包围物

        # 创建universe和geometry
        root_universe = openmc.Universe(cells=[fuel_cell, outside_cell])
        self.geometry = openmc.Geometry(root_universe)
        self.geometry.export_to_xml()  # 生成 geometry.xml

        # 设置模拟参数并生成 XML
        self.settings = openmc.Settings()
        self.settings.batches = 100
        self.settings.inactive = 10
        self.settings.particles = 1000
        self.settings.temperature = {'method': 'interpolation'}
        self.settings.export_to_xml()  # 生成 settings.xml
    
    def init_visualization(self):
        """初始化可视化图表"""
        plt.ion()  # 开启交互模式
        self.fig, self.axs = plt.subplots(2, 2, figsize=(15, 10))
        self.fig.suptitle('核反应堆运行状态监测', fontsize=16)
        
        # 初始化数据存储
        self.history = {
            'temperature': [],
            'power': [],
            'k_eff': [],
            'burnup': [],
            'control_rod': [],
            'steps': []
        }
        
        # 配置子图
        self.lines = {}
        
        # 温度和功率图
        self.axs[0, 0].set_title('温度和功率变化')
        self.axs[0, 0].set_xlabel('步数')
        self.lines['temp'], = self.axs[0, 0].plot([], [], 'r-', label='温度 (K)')
        self.lines['power'], = self.axs[0, 0].plot([], [], 'b-', label='功率 (MW)')
        self.axs[0, 0].legend()
        
        # k_eff图
        self.axs[0, 1].set_title('k_eff变化')
        self.axs[0, 1].set_xlabel('步数')
        self.lines['k_eff'], = self.axs[0, 1].plot([], [], 'g-', label='k_eff')
        self.axs[0, 1].axhline(y=1.0, color='r', linestyle='--', label='临界值')
        self.axs[0, 1].legend()
        
        # 控制棒位置图
        self.axs[1, 0].set_title('控制棒位置')
        self.axs[1, 0].set_xlabel('步数')
        self.lines['control'], = self.axs[1, 0].plot([], [], 'k-', label='控制棒位置')
        self.axs[1, 0].legend()
        
        # 燃耗图
        self.axs[1, 1].set_title('燃料燃耗')
        self.axs[1, 1].set_xlabel('步数')
        self.lines['burnup'], = self.axs[1, 1].plot([], [], 'm-', label='燃耗 (MWd/kgU)')
        self.axs[1, 1].legend()
        
        plt.tight_layout()

    def update_visualization(self):
        """更新可视化图表"""
        # 更新历史数据
        self.history['temperature'].append(self.temperature)
        self.history['power'].append(self.power)
        self.history['k_eff'].append(self.k_eff)
        self.history['burnup'].append(self.burnup)
        self.history['control_rod'].append(self.control_rod_pos)
        self.history['steps'].append(self.current_step)
        
        # 更新图表
        steps = self.history['steps']
        
        # 更新温度和功率
        self.lines['temp'].set_data(steps, self.history['temperature'])
        self.lines['power'].set_data(steps, self.history['power'])
        self.axs[0, 0].relim()
        self.axs[0, 0].autoscale_view()
        
        # 更新k_eff
        self.lines['k_eff'].set_data(steps, self.history['k_eff'])
        self.axs[0, 1].relim()
        self.axs[0, 1].autoscale_view()
        
        # 更新控制棒位置
        self.lines['control'].set_data(steps, self.history['control_rod'])
        self.axs[1, 0].relim()
        self.axs[1, 0].autoscale_view()
        
        # 更新燃耗
        self.lines['burnup'].set_data(steps, self.history['burnup'])
        self.axs[1, 1].relim()
        self.axs[1, 1].autoscale_view()
        
        plt.draw()
        plt.pause(0.01)

    def reset(self, seed=None):
        super().reset(seed=seed)
        self.current_step = 0
        self.temperature = 600  # K
        self.power = 1000      # MW
        self.k_eff = 1.0
        self.burnup = 0        # MWd/kgU
        self.control_rod_pos = 0.5
        
        # 初始化可视化
        self.init_visualization()
        
        return self._get_observation(), {}
    
    def _get_observation(self):
        return np.array([
            self.temperature,
            self.power,
            self.k_eff,
            self.control_rod_pos,
            self.burnup
        ], dtype=np.float32)
    
    def step(self, action):
        self.current_step += 1
        
        # 更新控制棒位置
        self.control_rod_pos = action / 10.0
        
        # 运行OpenMC模拟
        try:
            # 更新材料温度
            self.fuel.temperature = self.temperature
            
            # 运行临界计算
            openmc.run()
         
            # 获取k_eff
            with openmc.StatePoint("/home/unknowxxs/python/statepoint.100.h5") as sp:
                self.k_eff = sp.keff.nominal_value
                
            # 更新系统状态
            self._update_state()
            
            # 更新可视化
            self.update_visualization()
            
            # 计算奖励
            reward = self._calculate_reward()
            
            # 检查是否结束
            done = self.current_step >= self.max_steps or self._is_unsafe()
            
        except Exception as e:
            print(f"模拟过程出错: {e}")
            reward = -1000
            done = True
        
        return self._get_observation(), reward, done, False, {}
    
    def _update_state(self):
        # 基于k_eff更新功率
        power_factor = (self.k_eff - 1.0) * 5000
        self.power = np.clip(self.power + power_factor, 0, 3000)
        
        # 更新温度
        delta_temp = (self.power - 1000) * 0.1
        self.temperature = np.clip(self.temperature + delta_temp, 500, 1500)
        
        # 更新燃耗
        self.burnup += self.power * 0.001
        
    def _calculate_reward(self):
        reward = 0
        
        # 奖励稳定的运行状态
        reward -= abs(self.power - 1000) * 0.01  # 目标功率1000MW
        reward -= abs(self.k_eff - 1.0) * 100    # 目标k_eff=1
        reward -= abs(self.temperature - 900) * 0.1  # 目标温度900K
        
        # 惩罚不安全状态
        if self._is_unsafe():
            reward -= 1000
            
        return reward
    
    def _is_unsafe(self):
        return (self.temperature > 1400 or 
                self.power > 2500 or 
                self.k_eff > 1.2)

def train_agent():
    env = make_vec_env(NuclearReactorEnv, n_envs=4)
    
    model = PPO(
        "MlpPolicy",
        env,
        verbose=1,
        learning_rate=0.0003,
        n_steps=2048,
        batch_size=64,
        n_epochs=10
    )
    
    model.learn(total_timesteps=1000)
    model.save("ppo_nuclear_reactor")
    
    return model

def evaluate_agent(model, num_episodes=5):
    env = NuclearReactorEnv()
    
    for episode in range(num_episodes):
        obs, _ = env.reset()
        episode_reward = 0
        done = False
        
        while not done:
            action, _ = model.predict(obs)
            obs, reward, done, _, _ = env.step(action)
            episode_reward += reward
            
            # 打印当前状态
            print(f"步骤 {env.current_step}:")
            print(f"温度: {env.temperature:.1f}K")
            print(f"功率: {env.power:.1f}MW")
            print(f"k_eff: {env.k_eff:.3f}")
            print(f"控制棒位置: {env.control_rod_pos:.2f}")
            print(f"燃耗: {env.burnup:.2f}MWd/kgU")
            print("------------------------")
        
        plt.savefig(f'episode_{episode+1}_results.png')
        print(f"Episode {episode + 1}: 总奖励 = {episode_reward:.2f}\n")
    
    plt.close('all')

model = train_agent()
print("\n评估核反应堆控制性能：")
evaluate_agent(model)

```
Assistant: