Edge computing nodes undertake an increasing number of tasks with the rise of business density.Therefore,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical ...Edge computing nodes undertake an increasing number of tasks with the rise of business density.Therefore,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical challenge.This study proposes an edge task scheduling approach based on an improved Double Deep Q Network(DQN),which is adopted to separate the calculations of target Q values and the selection of the action in two networks.A new reward function is designed,and a control unit is added to the experience replay unit of the agent.The management of experience data are also modified to fully utilize its value and improve learning efficiency.Reinforcement learning agents usually learn from an ignorant state,which is inefficient.As such,this study proposes a novel particle swarm optimization algorithm with an improved fitness function,which can generate optimal solutions for task scheduling.These optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition level.The proposed algorithm is compared with six other methods in simulation experiments.Results show that the proposed algorithm outperforms other benchmark methods regarding makespan.展开更多
Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment.Reinforcement Learning methods enable a st...Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment.Reinforcement Learning methods enable a state action function in mobile robots suited to their environment.During trial-and-error interaction with its surroundings,it helps a robot tofind an ideal behavior on its own.The Deep Q Network(DQN)algorithm is used in TurtleBot 3(TB3)to achieve the goal by successfully avoiding the obstacles.But it requires a large number of training iterations.This research mainly focuses on a mobility robot’s best path prediction utilizing DQN and the Artificial Potential Field(APF)algorithms.First,a TB3 Waffle Pi DQN is built and trained to reach the goal.Then the APF shortest path algorithm is incorporated into the DQN algorithm.The proposed planning approach is compared with the standard DQN method in a virtual environment based on the Robot Operation System(ROS).The results from the simulation show that the combination is effective for DQN and APF gives a better optimal path and takes less time when compared to the conventional DQN algo-rithm.The performance improvement rate of the proposed DQN+APF in comparison with DQN in terms of the number of successful targets is attained by 88%.The performance of the proposed DQN+APF in comparison with DQN in terms of average time is achieved by 0.331 s.The performance of the proposed DQN+APF in comparison with DQN average rewards in which the positive goal is attained by 85%and the negative goal is attained by-90%.展开更多
To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on...To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long shortterm memory(RPP-LSTM)network is proposed,which combines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning algorithm.LSTM networks are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some memory.Thanks to LSTM network,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment.Besides,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path planning.Simulation verification shows that compared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.展开更多
针对5G新空口-车联网(New Radio-Vehicle to Everything,NR-V2X)场景下车对基础设施(Vehicle to Infrastructure,V2I)和车对车(Vehicle to Vehicle,V2V)共享上行通信链路的频谱资源分配问题,提出了一种联邦-多智能体深度Q网络(Federated...针对5G新空口-车联网(New Radio-Vehicle to Everything,NR-V2X)场景下车对基础设施(Vehicle to Infrastructure,V2I)和车对车(Vehicle to Vehicle,V2V)共享上行通信链路的频谱资源分配问题,提出了一种联邦-多智能体深度Q网络(Federated Learning-Multi-Agent Deep Q Network,FL-MADQN)算法.该分布式算法中,每个车辆用户作为一个智能体,根据获取的本地信道状态信息,以网络信道容量最佳为目标函数,采用DQN算法训练学习本地网络模型.采用联邦学习加快以及稳定各智能体网络模型训练的收敛速度,即将各智能体的本地模型上传至基站进行聚合形成全局模型,再将全局模型下发至各智能体更新本地模型.仿真结果表明:与传统分布式多智能体DQN算法相比,所提出的方案具有更快的模型收敛速度,并且当车辆用户数增大时仍然保证V2V链路的通信效率以及V2I链路的信道容量.展开更多
基金supported by the National Key Research and Development Program of China(No.2021YFE0116900)National Natural Science Foundation of China(Nos.42275157,62002276,and 41975142)Major Program of the National Social Science Fund of China(No.17ZDA092).
文摘Edge computing nodes undertake an increasing number of tasks with the rise of business density.Therefore,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical challenge.This study proposes an edge task scheduling approach based on an improved Double Deep Q Network(DQN),which is adopted to separate the calculations of target Q values and the selection of the action in two networks.A new reward function is designed,and a control unit is added to the experience replay unit of the agent.The management of experience data are also modified to fully utilize its value and improve learning efficiency.Reinforcement learning agents usually learn from an ignorant state,which is inefficient.As such,this study proposes a novel particle swarm optimization algorithm with an improved fitness function,which can generate optimal solutions for task scheduling.These optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition level.The proposed algorithm is compared with six other methods in simulation experiments.Results show that the proposed algorithm outperforms other benchmark methods regarding makespan.
文摘Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment.Reinforcement Learning methods enable a state action function in mobile robots suited to their environment.During trial-and-error interaction with its surroundings,it helps a robot tofind an ideal behavior on its own.The Deep Q Network(DQN)algorithm is used in TurtleBot 3(TB3)to achieve the goal by successfully avoiding the obstacles.But it requires a large number of training iterations.This research mainly focuses on a mobility robot’s best path prediction utilizing DQN and the Artificial Potential Field(APF)algorithms.First,a TB3 Waffle Pi DQN is built and trained to reach the goal.Then the APF shortest path algorithm is incorporated into the DQN algorithm.The proposed planning approach is compared with the standard DQN method in a virtual environment based on the Robot Operation System(ROS).The results from the simulation show that the combination is effective for DQN and APF gives a better optimal path and takes less time when compared to the conventional DQN algo-rithm.The performance improvement rate of the proposed DQN+APF in comparison with DQN in terms of the number of successful targets is attained by 88%.The performance of the proposed DQN+APF in comparison with DQN in terms of average time is achieved by 0.331 s.The performance of the proposed DQN+APF in comparison with DQN average rewards in which the positive goal is attained by 85%and the negative goal is attained by-90%.
基金supported by the Natural Science Basic Research Prog ram of Shaanxi(2022JQ-593)。
文摘To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long shortterm memory(RPP-LSTM)network is proposed,which combines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning algorithm.LSTM networks are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some memory.Thanks to LSTM network,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment.Besides,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path planning.Simulation verification shows that compared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.
文摘针对5G新空口-车联网(New Radio-Vehicle to Everything,NR-V2X)场景下车对基础设施(Vehicle to Infrastructure,V2I)和车对车(Vehicle to Vehicle,V2V)共享上行通信链路的频谱资源分配问题,提出了一种联邦-多智能体深度Q网络(Federated Learning-Multi-Agent Deep Q Network,FL-MADQN)算法.该分布式算法中,每个车辆用户作为一个智能体,根据获取的本地信道状态信息,以网络信道容量最佳为目标函数,采用DQN算法训练学习本地网络模型.采用联邦学习加快以及稳定各智能体网络模型训练的收敛速度,即将各智能体的本地模型上传至基站进行聚合形成全局模型,再将全局模型下发至各智能体更新本地模型.仿真结果表明:与传统分布式多智能体DQN算法相比,所提出的方案具有更快的模型收敛速度,并且当车辆用户数增大时仍然保证V2V链路的通信效率以及V2I链路的信道容量.
文摘针对传统深度Q学习网络(deep Q-learning network,DQN)在具有动态障碍物的路径规划下,移动机器人在探索时频繁碰撞难以移动至目标点的问题,通过在探索策略和经验回放机制上进行改进,提出一种改进的DQN算法。在探索策略上,利用快速搜索随机树(rapidly-exploring random tree,RRT)算法自动生成静态先验知识来指导动作选取,替代ε-贪婪策略的随机动作,提高智能体到达目标的成功率;在经验利用上,使用K-means算法设计一种聚类经验回放机制,根据动态障碍物的位置信息进行聚类分簇,着重采样与当前智能体状态相似的经验进行回放,使智能体更有效地避免碰撞动态障碍物。二维栅格化环境下的仿真实验表明,在动态环境下,该算法可以避开静态和动态障碍物,成功移动至目标点,验证了该算法在应对动态避障路径规划的可行性。
