#include \n#include \n#include \n#include \n#include \n#include <condition_variable>\n\n// 定义全局共享的神经网络模型\nclass Model {\npublic:\n // 用于更新模型参数的方法\n void update(std::vector& gradients) {\n std::lock_guardstd::mutex lock(mutex_);\n for (int i = 0; i < parameters_.size(); ++i) {\n parameters_[i] += learning_rate_ * gradients[i];\n }\n }\n \n // 用于获取模型参数的方法\n std::vector getParameters() {\n std::lock_guardstd::mutex lock(mutex_);\n return parameters_;\n }\n \nprivate:\n std::mutex mutex_;\n std::vector parameters_; // 模型参数\n double learning_rate_ = 0.001; // 学习率\n};\n\n// 定义无人机图像识别的环境\nclass Environment {\npublic:\n double step(int action) {\n // 执行动作并返回奖励\n // ...\n }\n \n // 获取当前图像状态\n std::vector getState() {\n // 获取当前图像状态\n // ...\n }\n};\n\n// 定义A3C算法的Actor\nclass Actor {\npublic:\n Actor(Model& model) : model_(model) {}\n \n void run(Environment& env, std::atomic& terminate) {\n while (!terminate) {\n std::vector gradients;\n std::vector state = env.getState();\n int action = sampleAction(state);\n double reward = env.step(action);\n std::vector nextState = env.getState();\n \n // 计算梯度\n gradients = computeGradients(state, action, reward, nextState);\n \n // 更新模型\n model_.update(gradients);\n }\n }\n \nprivate:\n Model& model_;\n \n // 根据当前状态采样动作\n int sampleAction(std::vector& state) {\n // 采样动作\n // ...\n }\n \n // 根据当前状态、动作、奖励和下一个状态计算梯度\n std::vector computeGradients(std::vector& state, int action, double reward, std::vector& nextState) {\n // 计算梯度\n // ...\n }\n};\n\n// 定义A3C算法的Critic\nclass Critic {\npublic:\n Critic(Model& model) : model_(model) {}\n \n void run(Environment& env, std::atomic& terminate) {\n while (!terminate) {\n std::vector state = env.getState();\n int action = sampleAction(state);\n double reward = env.step(action);\n std::vector nextState = env.getState();\n \n // 更新模型\n updateModel(state, action, reward, nextState);\n }\n }\n \nprivate:\n Model& model_;\n \n // 根据当前状态采样动作\n int sampleAction(std::vector& state) {\n // 采样动作\n // ...\n }\n \n // 根据当前状态、动作、奖励和下一个状态更新模型\n void updateModel(std::vector& state, int action, double reward, std::vector& nextState) {\n // 更新模型\n // ...\n }\n};\n\n// 主函数\nint main() {\n std::atomic terminate(false);\n Model model;\n Environment env;\n Actor actor(model);\n Critic critic(model);\n \n // 创建Actor和Critic线程\n std::thread actorThread(& {\n actor.run(env, terminate);\n });\n \n std::thread criticThread(& {\n critic.run(env, terminate);\n });\n \n // 运行一段时间后停止并等待线程结束\n std::this_thread::sleep_for(std::chrono::seconds(10));\n terminate = true;\n actorThread.join();\n criticThread.join();\n \n return 0;\n}\n
