Opportunistic mobile crowdsensing(MCS)non-intrusively exploits human mobility trajectories,and the participants’smart devices as sensors have become promising paradigms for various urban data acquisition tasks.Howeve...Opportunistic mobile crowdsensing(MCS)non-intrusively exploits human mobility trajectories,and the participants’smart devices as sensors have become promising paradigms for various urban data acquisition tasks.However,in practice,opportunistic MCS has several challenges from both the perspectives of MCS participants and the data platform.On the one hand,participants face uncertainties in conducting MCS tasks,including their mobility and implicit interactions among participants,and participants’economic returns given by the MCS data platform are determined by not only their own actions but also other participants’strategic actions.On the other hand,the platform can only observe the participants’uploaded sensing data that depends on the unknown effort/action exerted by participants to the platform,while,for optimizing its overall objective,the platform needs to properly reward certain participants for incentivizing them to provide high-quality data.To address the challenge of balancing individual incentives and platform objectives in MCS,this paper proposes MARCS,an online sensing policy based on multi-agent deep reinforcement learning(MADRL)with centralized training and decentralized execution(CTDE).Specifically,the interactions between MCS participants and the data platform are modeled as a partially observable Markov game,where participants,acting as agents,use DRL-based policies to make decisions based on local observations,such as task trajectories and platform payments.To align individual and platform goals effectively,the platform leverages Shapley value to estimate the contribution of each participant’s sensed data,using these estimates as immediate rewards to guide agent training.The experimental results on real mobility trajectory datasets indicate that the revenue of MARCS reaches almost 35%,53%,and 100%higher than DDPG,Actor-Critic,and model predictive control(MPC)respectively on the participant side and similar results on the platform side,which show superior performance compared to baselines.展开更多
In Heterogeneous Vehicle-to-Everything Networks(HVNs),multiple users such as vehicles and handheld devices and infrastructure can communicate with each other to obtain more advanced services.However,the increasing num...In Heterogeneous Vehicle-to-Everything Networks(HVNs),multiple users such as vehicles and handheld devices and infrastructure can communicate with each other to obtain more advanced services.However,the increasing number of entities accessing HVNs presents a huge technical challenge to allocate the limited wireless resources.Traditional model-driven resource allocation approaches are no longer applicable because of rich data and the interference problem of multiple communication modes reusing resources in HVNs.In this paper,we investigate a wireless resource allocation scheme including power control and spectrum allocation based on the resource block reuse strategy.To meet the high capacity of cellular users and the high reliability of Vehicle-to-Vehicle(V2V)user pairs,we propose a data-driven Multi-Agent Deep Reinforcement Learning(MADRL)resource allocation scheme for the HVN.Simulation results demonstrate that compared to existing algorithms,the proposed MADRL-based scheme achieves a high sum capacity and probability of successful V2V transmission,while providing close-to-limit performance.展开更多
This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary obj...This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.展开更多
Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-...Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-robot control.Empowering cooperative MARL with multi-task decision-making capabilities is expected to further broaden its application scope.In multi-task scenarios,cooperative MARL algorithms need to address 3 types of multi-task problems:reward-related multi-task,arising from different reward functions;multi-domain multi-task,caused by differences in state and action spaces,state transition functions;and scalability-related multi-task,resulting from the dynamic variation in the number of agents.Most existing studies focus on scalability-related multitask problems.However,with the increasing integration between large language models(LLMs)and multi-agent systems,a growing number of LLM-based multi-agent systems have emerged,enabling more complex multi-task cooperation.This paper provides a comprehensive review of the latest advances in this field.By combining multi-task reinforcement learning with cooperative MARL,we categorize and analyze the 3 major types of multi-task problems under multi-agent settings,offering more fine-grained classifications and summarizing key insights for each.In addition,we summarize commonly used benchmarks and discuss future directions of research in this area,which hold promise for further enhancing the multi-task cooperation capabilities of multi-agent systems and expanding their practical applications in the real world.展开更多
Cybertwin-enabled 6th Generation(6G)network is envisioned to support artificial intelligence-native management to meet changing demands of 6G applications.Multi-Agent Deep Reinforcement Learning(MADRL)technologies dri...Cybertwin-enabled 6th Generation(6G)network is envisioned to support artificial intelligence-native management to meet changing demands of 6G applications.Multi-Agent Deep Reinforcement Learning(MADRL)technologies driven by Cybertwins have been proposed for adaptive task offloading strategies.However,the existence of random transmission delay between Cybertwin-driven agents and underlying networks is not considered in related works,which destroys the standard Markov property and increases the decision reaction time to reduce the task offloading strategy performance.In order to address this problem,we propose a pipelining task offloading method to lower the decision reaction time and model it as a delay-aware Markov Decision Process(MDP).Then,we design a delay-aware MADRL algorithm to minimize the weighted sum of task execution latency and energy consumption.Firstly,the state space is augmented using the lastly-received state and historical actions to rebuild the Markov property.Secondly,Gate Transformer-XL is introduced to capture historical actions'importance and maintain the consistent input dimension dynamically changed due to random transmission delays.Thirdly,a sampling method and a new loss function with the difference between the current and target state value and the difference between real state-action value and augmented state-action value are designed to obtain state transition trajectories close to the real ones.Numerical results demonstrate that the proposed methods are effective in reducing reaction time and improving the task offloading performance in the random-delay Cybertwin-enabled 6G networks.展开更多
In recent years,significant research attention has been directed towards swarm intelligence.The Milling behavior of fish schools,a prime example of swarm intelligence,shows how simple rules followed by individual agen...In recent years,significant research attention has been directed towards swarm intelligence.The Milling behavior of fish schools,a prime example of swarm intelligence,shows how simple rules followed by individual agents lead to complex collective behaviors.This paper studies Multi-Agent Reinforcement Learning to simulate fish schooling behavior,overcoming the challenges of tuning parameters in traditional models and addressing the limitations of single-agent methods in multi-agent environments.Based on this foundation,a novel Graph Convolutional Networks(GCN)-Critic MADDPG algorithm leveraging GCN is proposed to enhance cooperation among agents in a multi-agent system.Simulation experiments demonstrate that,compared to traditional single-agent algorithms,the proposed method not only exhibits significant advantages in terms of convergence speed and stability but also achieves tighter group formations and more naturally aligned Milling behavior.Additionally,a fish school self-organizing behavior research platform based on an event-triggered mechanism has been developed,providing a robust tool for exploring dynamic behavioral changes under various conditions.展开更多
Moving Target Defense(MTD)necessitates scientifically effective decision-making methodologies for defensive technology implementation.While most MTD decision studies focus on accurately identifying optimal strategies,...Moving Target Defense(MTD)necessitates scientifically effective decision-making methodologies for defensive technology implementation.While most MTD decision studies focus on accurately identifying optimal strategies,the issue of optimal defense timing remains underexplored.Current default approaches—periodic or overly frequent MTD triggers—lead to suboptimal trade-offs among system security,performance,and cost.The timing of MTD strategy activation critically impacts both defensive efficacy and operational overhead,yet existing frameworks inadequately address this temporal dimension.To bridge this gap,this paper proposes a Stackelberg-FlipIt game model that formalizes asymmetric cyber conflicts as alternating control over attack surfaces,thereby capturing the dynamic security state evolution of MTD systems.We introduce a belief factor to quantify information asymmetry during adversarial interactions,enhancing the precision of MTD trigger timing.Leveraging this game-theoretic foundation,we employMulti-Agent Reinforcement Learning(MARL)to derive adaptive temporal strategies,optimized via a novel four-dimensional reward function that holistically balances security,performance,cost,and timing.Experimental validation using IP addressmutation against scanning attacks demonstrates stable strategy convergence and accelerated defense response,significantly improving cybersecurity affordability and effectiveness.展开更多
The Internet of Unmanned Aerial Vehicles(I-UAVs)is expected to execute latency-sensitive tasks,but limited by co-channel interference and malicious jamming.In the face of unknown prior environmental knowledge,defendin...The Internet of Unmanned Aerial Vehicles(I-UAVs)is expected to execute latency-sensitive tasks,but limited by co-channel interference and malicious jamming.In the face of unknown prior environmental knowledge,defending against jamming and interference through spectrum allocation becomes challenging,especially when each UAV pair makes decisions independently.In this paper,we propose a cooperative multi-agent reinforcement learning(MARL)-based anti-jamming framework for I-UAVs,enabling UAV pairs to learn their own policies cooperatively.Specifically,we first model the problem as a modelfree multi-agent Markov decision process(MAMDP)to maximize the long-term expected system throughput.Then,for improving the exploration of the optimal policy,we resort to optimizing a MARL objective function with a mutual-information(MI)regularizer between states and actions,which can dynamically assign the probability for actions frequently used by the optimal policy.Next,through sharing their current channel selections and local learning experience(their soft Q-values),the UAV pairs can learn their own policies cooperatively relying on only preceding observed information and predicting others’actions.Our simulation results show that for both sweep jamming and Markov jamming patterns,the proposed scheme outperforms the benchmarkers in terms of throughput,convergence and stability for different numbers of jammers,channels and UAV pairs.展开更多
This paper proposes a Multi-Agent Attention Proximal Policy Optimization(MA2PPO)algorithm aiming at the problems such as credit assignment,low collaboration efficiency and weak strategy generalization ability existing...This paper proposes a Multi-Agent Attention Proximal Policy Optimization(MA2PPO)algorithm aiming at the problems such as credit assignment,low collaboration efficiency and weak strategy generalization ability existing in the cooperative pursuit tasks of multiple unmanned aerial vehicles(UAVs).Traditional algorithms often fail to effectively identify critical cooperative relationships in such tasks,leading to low capture efficiency and a significant decline in performance when the scale expands.To tackle these issues,based on the proximal policy optimization(PPO)algorithm,MA2PPO adopts the centralized training with decentralized execution(CTDE)framework and introduces a dynamic decoupling mechanism,that is,sharing the multi-head attention(MHA)mechanism for critics during centralized training to solve the credit assignment problem.This method enables the pursuers to identify highly correlated interactions with their teammates,effectively eliminate irrelevant and weakly relevant interactions,and decompose large-scale cooperation problems into decoupled sub-problems,thereby enhancing the collaborative efficiency and policy stability among multiple agents.Furthermore,a reward function has been devised to facilitate the pursuers to encircle the escapee by combining a formation reward with a distance reward,which incentivizes UAVs to develop sophisticated cooperative pursuit strategies.Experimental results demonstrate the effectiveness of the proposed algorithm in achieving multi-UAV cooperative pursuit and inducing diverse cooperative pursuit behaviors among UAVs.Moreover,experiments on scalability have demonstrated that the algorithm is suitable for large-scale multi-UAV systems.展开更多
Policy training against diverse opponents remains a challenge when using Multi-Agent Reinforcement Learning(MARL)in multiple Unmanned Combat Aerial Vehicle(UCAV)air combat scenarios.In view of this,this paper proposes...Policy training against diverse opponents remains a challenge when using Multi-Agent Reinforcement Learning(MARL)in multiple Unmanned Combat Aerial Vehicle(UCAV)air combat scenarios.In view of this,this paper proposes a novel Dominant and Non-dominant strategy sample selection(DoNot)mechanism and a Local Observation Enhanced Multi-Agent Proximal Policy Optimization(LOE-MAPPO)algorithm to train the multi-UCAV air combat policy and improve its generalization.Specifically,the LOE-MAPPO algorithm adopts a mixed state that concatenates the global state and individual agent's local observation to enable efficient value function learning in multi-UCAV air combat.The DoNot mechanism classifies opponents into dominant or non-dominant strategy opponents,and samples from easier to more challenging opponents to form an adaptive training curriculum.Empirical results demonstrate that the proposed LOE-MAPPO algorithm outperforms baseline MARL algorithms in multi-UCAV air combat scenarios,and the DoNot mechanism leads to stronger policy generalization when facing diverse opponents.The results pave the way for the fast generation of cooperative strategies for air combat agents with MARLalgorithms.展开更多
The rapid development of the Internet of Vehicles(IoVs)underscores the importance of Vehicle-to-Everything(V2X)communication for ensuring driving safety.V2X supports control systems by providing reliable and real-time...The rapid development of the Internet of Vehicles(IoVs)underscores the importance of Vehicle-to-Everything(V2X)communication for ensuring driving safety.V2X supports control systems by providing reliable and real-time information,while the control system's decisions,in turn,affect the communication topology and channel state.Depending on the coupling between communication and control,radio resource allocation(RRA)should be controlaware.However,current RRA methods often focus on optimizing communication metrics,neglecting the needs of the control system.To promote the co-design of communication and control,this paper proposes a novel RRA method that integrates both communication and control considerations.From the communication perspective,the Age of Information(AoI)is introduced to measure the freshness of packets.From the control perspective,a weighted utility function based on Time-to-Collision(TTC)and driving distance is designed,emphasizing the neighboring importance and potentially dangerous vehicles.By synthesizing these two metrics,an optimization objective minimizing weighted AoI based on TTC and driving distance is formulated.The RRA process is modeled as a partially observable Markov decision process,and a multi-agent reinforcement learning algorithm incorporating positional encoding and attention mechanisms(PAMARL)is proposed.Simulation results show that PAMARL can reduce Collision Risk(CR)with better Packet Delivery Ratio(PDR)than others.展开更多
This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant(VPP)networks using multi-agent reinforcement learning(MARL).As the energy landscape evolves towards grea...This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant(VPP)networks using multi-agent reinforcement learning(MARL).As the energy landscape evolves towards greater decentralization and renewable integration,traditional optimization methods struggle to address the inherent complexities and uncertainties.Our proposed MARL framework enables adaptive,decentralized decision-making for both the distribution system operator and individual VPPs,optimizing economic efficiency while maintaining grid stability.We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay.Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods,including Stackelberg game models and model predictive control,achieving an 18.73%reduction in costs and a 22.46%increase in VPP profits.The MARL framework shows particular strength in scenarios with high renewable energy penetration,where it improves system performance by 11.95%compared with traditional methods.Furthermore,our approach demonstrates superior adaptability to unexpected events and mis-predictions,highlighting its potential for real-world implementation.展开更多
Conflict resolution(CR)is a fundamental component of air traffic management,where recent progress in artificial intelligence has led to the effective application of deep reinforcement learning(DRL)techniques to enhanc...Conflict resolution(CR)is a fundamental component of air traffic management,where recent progress in artificial intelligence has led to the effective application of deep reinforcement learning(DRL)techniques to enhance CR strategies.However,existing DRL models applied to CR are often limited to simple scenarios.This approach frequently leads to the neglect of the high risks associated with multiple intersections in the high-density and multi-airport system terminal area(MAS-TMA),and suffers from poor interpretability.This paper addresses the aforementioned gap by introducing an improved multi-agent DRL model that adopted to autonomous CR(AutoCR)within MAS-TMA.Specifically,dynamic weather conditions are incorporated into the state space to enhance adaptability.In the action space,the flight intent is considered and transformed into optimal maneuvers according to overload,thus improving interpretability.On these bases,the deep Q-network(DQN)algorithm is further improved to address the AutoCR problem in MAS-TMA.Simulation experiments conducted in the“Guangdong-Hong Kong-Macao”greater bay area(GBA)MAS-TMA demonstrate the effectiveness of the proposed method,successfully resolving over eight potential conflicts and performing robustly across various air traffic densities.展开更多
Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metavers...Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.展开更多
Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision u...Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was proposed.First,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision space.Second,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method.Simulation results show that the proposed model has good performance and low computational resource requirement.The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.展开更多
Aiming at the rapid growth of network services,which leads to the problems of long service request processing time and high deployment cost in the deployment of network function virtualization service function chain(S...Aiming at the rapid growth of network services,which leads to the problems of long service request processing time and high deployment cost in the deployment of network function virtualization service function chain(SFC)under 5G networks,this paper proposes a multi-agent deep deterministic policy gradient optimization algorithm for SFC deployment(MADDPG-SD).Initially,an optimization model is devised to enhance the request acceptance rate,minimizing the latency and deploying the cost SFC is constructed for the network resource-constrained case.Subsequently,we model the dynamic problem as a Markov decision process(MDP),facilitating adaptation to the evolving states of network resources.Finally,by allocating SFCs to different agents and adopting a collaborative deployment strategy,each agent aims to maximize the request acceptance rate or minimize latency and costs.These agents learn strategies from historical data of virtual network functions in SFCs to guide server node selection,and achieve approximately optimal SFC deployment strategies through a cooperative framework of centralized training and distributed execution.Experimental simulation results indicate that the proposed method,while simultaneously meeting performance requirements and resource capacity constraints,has effectively increased the acceptance rate of requests compared to the comparative algorithms,reducing the end-to-end latency by 4.942%and the deployment cost by 8.045%.展开更多
This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved pr...This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved proximal policy optimization(IPPO)method to make real-time decisions for the DHHBFSP.A multi-objective Markov decision process is modeled for the DHHBFSP,where the reward function is represented by a vector with dynamic weights instead of the common objectiverelated scalar value.A factory agent(FA)is formulated for each factory to select unscheduled jobs and is trained by the proposed IPPO to improve the decision quality.Multiple FAs work asynchronously to allocate jobs that arrive randomly at the shop.A two-stage training strategy is introduced in the IPPO,which learns from both single-and dual-policy data for better data utilization.The proposed IPPO is tested on randomly generated instances and compared with variants of the basic proximal policy optimization(PPO),dispatch rules,multi-objective metaheuristics,and multi-agent reinforcement learning methods.Extensive experimental results suggest that the proposed strategies offer significant improvements to the basic PPO,and the proposed IPPO outperforms the state-of-the-art scheduling methods in both convergence and solution quality.展开更多
Unmanned Aerial Vehicle(UAV)stands as a burgeoning electric transportation carrier,holding substantial promise for the logistics sector.A reinforcement learning framework Centralized-S Proximal Policy Optimization(C-S...Unmanned Aerial Vehicle(UAV)stands as a burgeoning electric transportation carrier,holding substantial promise for the logistics sector.A reinforcement learning framework Centralized-S Proximal Policy Optimization(C-SPPO)based on centralized decision process and considering policy entropy(S)is proposed.The proposed framework aims to plan the best scheduling scheme with the objective of minimizing both the timeout of order requests and the flight impact of UAVs that may lead to conflicts.In this framework,the intents of matching act are generated through the observations of UAV agents,and the ultimate conflict-free matching results are output under the guidance of a centralized decision maker.Concurrently,a pre-activation operation is introduced to further enhance the cooperation among UAV agents.Simulation experiments based on real-world data from New York City are conducted.The results indicate that the proposed CSPPO outperforms the baseline algorithms in the Average Delay Time(ADT),the Maximum Delay Time(MDT),the Order Delay Rate(ODR),the Average Flight Distance(AFD),and the Flight Impact Ratio(FIR).Furthermore,the framework demonstrates scalability to scenarios of different sizes without requiring additional training.展开更多
The Virtual Power Plant(VPP),as an innovative power management architecture,achieves flexible dispatch and resource optimization of power systems by integrating distributed energy resources.However,due to significant ...The Virtual Power Plant(VPP),as an innovative power management architecture,achieves flexible dispatch and resource optimization of power systems by integrating distributed energy resources.However,due to significant differences in operational costs and flexibility of various types of generation resources,as well as the volatility and uncertainty of renewable energy sources(such as wind and solar power)and the complex variability of load demand,the scheduling optimization of virtual power plants has become a critical issue that needs to be addressed.To solve this,this paper proposes an intelligent scheduling method for virtual power plants based on Deep Reinforcement Learning(DRL),utilizing Deep Q-Networks(DQN)for real-time optimization scheduling of dynamic peaking unit(DPU)and stable baseload unit(SBU)in the virtual power plant.By modeling the scheduling problem as a Markov Decision Process(MDP)and designing an optimization objective function that integrates both performance and cost,the scheduling efficiency and economic performance of the virtual power plant are significantly improved.Simulation results show that,compared with traditional scheduling methods and other deep reinforcement learning algorithms,the proposed method demonstrates significant advantages in key performance indicators:response time is shortened by up to 34%,task success rate is increased by up to 46%,and costs are reduced by approximately 26%.Experimental results verify the efficiency and scalability of the method under complex load environments and the volatility of renewable energy,providing strong technical support for the intelligent scheduling of virtual power plants.展开更多
The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability an...The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.展开更多
基金sponsored by Qinglan Project of Jiangsu Province,and Jiangsu Provincial Key Research and Development Program(No.BE2020084-1).
文摘Opportunistic mobile crowdsensing(MCS)non-intrusively exploits human mobility trajectories,and the participants’smart devices as sensors have become promising paradigms for various urban data acquisition tasks.However,in practice,opportunistic MCS has several challenges from both the perspectives of MCS participants and the data platform.On the one hand,participants face uncertainties in conducting MCS tasks,including their mobility and implicit interactions among participants,and participants’economic returns given by the MCS data platform are determined by not only their own actions but also other participants’strategic actions.On the other hand,the platform can only observe the participants’uploaded sensing data that depends on the unknown effort/action exerted by participants to the platform,while,for optimizing its overall objective,the platform needs to properly reward certain participants for incentivizing them to provide high-quality data.To address the challenge of balancing individual incentives and platform objectives in MCS,this paper proposes MARCS,an online sensing policy based on multi-agent deep reinforcement learning(MADRL)with centralized training and decentralized execution(CTDE).Specifically,the interactions between MCS participants and the data platform are modeled as a partially observable Markov game,where participants,acting as agents,use DRL-based policies to make decisions based on local observations,such as task trajectories and platform payments.To align individual and platform goals effectively,the platform leverages Shapley value to estimate the contribution of each participant’s sensed data,using these estimates as immediate rewards to guide agent training.The experimental results on real mobility trajectory datasets indicate that the revenue of MARCS reaches almost 35%,53%,and 100%higher than DDPG,Actor-Critic,and model predictive control(MPC)respectively on the participant side and similar results on the platform side,which show superior performance compared to baselines.
基金funded in part by the National Key Research and Development of China Project (2020YFB1807204)in part by National Natural Science Foundation of China (U2001213 and 61971191)+1 种基金in part by the Beijing Natural Science Foundation under Grant L201011in part by the key project of Natural Science Foundation of Jiangxi Province (20202ACBL202006)。
文摘In Heterogeneous Vehicle-to-Everything Networks(HVNs),multiple users such as vehicles and handheld devices and infrastructure can communicate with each other to obtain more advanced services.However,the increasing number of entities accessing HVNs presents a huge technical challenge to allocate the limited wireless resources.Traditional model-driven resource allocation approaches are no longer applicable because of rich data and the interference problem of multiple communication modes reusing resources in HVNs.In this paper,we investigate a wireless resource allocation scheme including power control and spectrum allocation based on the resource block reuse strategy.To meet the high capacity of cellular users and the high reliability of Vehicle-to-Vehicle(V2V)user pairs,we propose a data-driven Multi-Agent Deep Reinforcement Learning(MADRL)resource allocation scheme for the HVN.Simulation results demonstrate that compared to existing algorithms,the proposed MADRL-based scheme achieves a high sum capacity and probability of successful V2V transmission,while providing close-to-limit performance.
基金supported by the National Natural Science Foundation of China(Nos.12272104,U22B2013).
文摘This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.
基金The National Natural Science Foundation of China(62136008,62293541)The Beijing Natural Science Foundation(4232056)The Beijing Nova Program(20240484514).
文摘Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-robot control.Empowering cooperative MARL with multi-task decision-making capabilities is expected to further broaden its application scope.In multi-task scenarios,cooperative MARL algorithms need to address 3 types of multi-task problems:reward-related multi-task,arising from different reward functions;multi-domain multi-task,caused by differences in state and action spaces,state transition functions;and scalability-related multi-task,resulting from the dynamic variation in the number of agents.Most existing studies focus on scalability-related multitask problems.However,with the increasing integration between large language models(LLMs)and multi-agent systems,a growing number of LLM-based multi-agent systems have emerged,enabling more complex multi-task cooperation.This paper provides a comprehensive review of the latest advances in this field.By combining multi-task reinforcement learning with cooperative MARL,we categorize and analyze the 3 major types of multi-task problems under multi-agent settings,offering more fine-grained classifications and summarizing key insights for each.In addition,we summarize commonly used benchmarks and discuss future directions of research in this area,which hold promise for further enhancing the multi-task cooperation capabilities of multi-agent systems and expanding their practical applications in the real world.
基金funded by the National Key Research and Development Program of China under Grant 2019YFB1803301Beijing Natural Science Foundation (L202002)。
文摘Cybertwin-enabled 6th Generation(6G)network is envisioned to support artificial intelligence-native management to meet changing demands of 6G applications.Multi-Agent Deep Reinforcement Learning(MADRL)technologies driven by Cybertwins have been proposed for adaptive task offloading strategies.However,the existence of random transmission delay between Cybertwin-driven agents and underlying networks is not considered in related works,which destroys the standard Markov property and increases the decision reaction time to reduce the task offloading strategy performance.In order to address this problem,we propose a pipelining task offloading method to lower the decision reaction time and model it as a delay-aware Markov Decision Process(MDP).Then,we design a delay-aware MADRL algorithm to minimize the weighted sum of task execution latency and energy consumption.Firstly,the state space is augmented using the lastly-received state and historical actions to rebuild the Markov property.Secondly,Gate Transformer-XL is introduced to capture historical actions'importance and maintain the consistent input dimension dynamically changed due to random transmission delays.Thirdly,a sampling method and a new loss function with the difference between the current and target state value and the difference between real state-action value and augmented state-action value are designed to obtain state transition trajectories close to the real ones.Numerical results demonstrate that the proposed methods are effective in reducing reaction time and improving the task offloading performance in the random-delay Cybertwin-enabled 6G networks.
基金supported by the National Natural Science Foundation of China under Grant 62273351 and Grant 62303020.
文摘In recent years,significant research attention has been directed towards swarm intelligence.The Milling behavior of fish schools,a prime example of swarm intelligence,shows how simple rules followed by individual agents lead to complex collective behaviors.This paper studies Multi-Agent Reinforcement Learning to simulate fish schooling behavior,overcoming the challenges of tuning parameters in traditional models and addressing the limitations of single-agent methods in multi-agent environments.Based on this foundation,a novel Graph Convolutional Networks(GCN)-Critic MADDPG algorithm leveraging GCN is proposed to enhance cooperation among agents in a multi-agent system.Simulation experiments demonstrate that,compared to traditional single-agent algorithms,the proposed method not only exhibits significant advantages in terms of convergence speed and stability but also achieves tighter group formations and more naturally aligned Milling behavior.Additionally,a fish school self-organizing behavior research platform based on an event-triggered mechanism has been developed,providing a robust tool for exploring dynamic behavioral changes under various conditions.
基金funded by National Natural Science Foundation of China No.62302520.
文摘Moving Target Defense(MTD)necessitates scientifically effective decision-making methodologies for defensive technology implementation.While most MTD decision studies focus on accurately identifying optimal strategies,the issue of optimal defense timing remains underexplored.Current default approaches—periodic or overly frequent MTD triggers—lead to suboptimal trade-offs among system security,performance,and cost.The timing of MTD strategy activation critically impacts both defensive efficacy and operational overhead,yet existing frameworks inadequately address this temporal dimension.To bridge this gap,this paper proposes a Stackelberg-FlipIt game model that formalizes asymmetric cyber conflicts as alternating control over attack surfaces,thereby capturing the dynamic security state evolution of MTD systems.We introduce a belief factor to quantify information asymmetry during adversarial interactions,enhancing the precision of MTD trigger timing.Leveraging this game-theoretic foundation,we employMulti-Agent Reinforcement Learning(MARL)to derive adaptive temporal strategies,optimized via a novel four-dimensional reward function that holistically balances security,performance,cost,and timing.Experimental validation using IP addressmutation against scanning attacks demonstrates stable strategy convergence and accelerated defense response,significantly improving cybersecurity affordability and effectiveness.
基金supported in part by the National Natural Science Foundation of China under Grants 62001225,62071236,62071234 and U22A2002in part by the Major Science and Technology plan of Hainan Province under Grant ZDKJ2021022+1 种基金in part by the Scientific Research Fund Project of Hainan University under Grant KYQD(ZR)-21008in part by the Key Technologies R&D Program of Jiangsu(Prospective and Key Technologies for Industry)under Grants BE2023022 and BE2023022-2.
文摘The Internet of Unmanned Aerial Vehicles(I-UAVs)is expected to execute latency-sensitive tasks,but limited by co-channel interference and malicious jamming.In the face of unknown prior environmental knowledge,defending against jamming and interference through spectrum allocation becomes challenging,especially when each UAV pair makes decisions independently.In this paper,we propose a cooperative multi-agent reinforcement learning(MARL)-based anti-jamming framework for I-UAVs,enabling UAV pairs to learn their own policies cooperatively.Specifically,we first model the problem as a modelfree multi-agent Markov decision process(MAMDP)to maximize the long-term expected system throughput.Then,for improving the exploration of the optimal policy,we resort to optimizing a MARL objective function with a mutual-information(MI)regularizer between states and actions,which can dynamically assign the probability for actions frequently used by the optimal policy.Next,through sharing their current channel selections and local learning experience(their soft Q-values),the UAV pairs can learn their own policies cooperatively relying on only preceding observed information and predicting others’actions.Our simulation results show that for both sweep jamming and Markov jamming patterns,the proposed scheme outperforms the benchmarkers in terms of throughput,convergence and stability for different numbers of jammers,channels and UAV pairs.
基金supported by the National Research and Development Program of China under Grant JCKY2018607C019in part by the Key Laboratory Fund of UAV of Northwestern Polytechnical University under Grant 2021JCJQLB0710L.
文摘This paper proposes a Multi-Agent Attention Proximal Policy Optimization(MA2PPO)algorithm aiming at the problems such as credit assignment,low collaboration efficiency and weak strategy generalization ability existing in the cooperative pursuit tasks of multiple unmanned aerial vehicles(UAVs).Traditional algorithms often fail to effectively identify critical cooperative relationships in such tasks,leading to low capture efficiency and a significant decline in performance when the scale expands.To tackle these issues,based on the proximal policy optimization(PPO)algorithm,MA2PPO adopts the centralized training with decentralized execution(CTDE)framework and introduces a dynamic decoupling mechanism,that is,sharing the multi-head attention(MHA)mechanism for critics during centralized training to solve the credit assignment problem.This method enables the pursuers to identify highly correlated interactions with their teammates,effectively eliminate irrelevant and weakly relevant interactions,and decompose large-scale cooperation problems into decoupled sub-problems,thereby enhancing the collaborative efficiency and policy stability among multiple agents.Furthermore,a reward function has been devised to facilitate the pursuers to encircle the escapee by combining a formation reward with a distance reward,which incentivizes UAVs to develop sophisticated cooperative pursuit strategies.Experimental results demonstrate the effectiveness of the proposed algorithm in achieving multi-UAV cooperative pursuit and inducing diverse cooperative pursuit behaviors among UAVs.Moreover,experiments on scalability have demonstrated that the algorithm is suitable for large-scale multi-UAV systems.
文摘Policy training against diverse opponents remains a challenge when using Multi-Agent Reinforcement Learning(MARL)in multiple Unmanned Combat Aerial Vehicle(UCAV)air combat scenarios.In view of this,this paper proposes a novel Dominant and Non-dominant strategy sample selection(DoNot)mechanism and a Local Observation Enhanced Multi-Agent Proximal Policy Optimization(LOE-MAPPO)algorithm to train the multi-UCAV air combat policy and improve its generalization.Specifically,the LOE-MAPPO algorithm adopts a mixed state that concatenates the global state and individual agent's local observation to enable efficient value function learning in multi-UCAV air combat.The DoNot mechanism classifies opponents into dominant or non-dominant strategy opponents,and samples from easier to more challenging opponents to form an adaptive training curriculum.Empirical results demonstrate that the proposed LOE-MAPPO algorithm outperforms baseline MARL algorithms in multi-UCAV air combat scenarios,and the DoNot mechanism leads to stronger policy generalization when facing diverse opponents.The results pave the way for the fast generation of cooperative strategies for air combat agents with MARLalgorithms.
基金supported by Beijing Natural Science Foundation under Grant L202018the National Natural Science Foundation of China under Grant 61931005+1 种基金the Key Laboratory of Internet of Vehicle Technical Innovation and Testing(CAICT),Ministry of Industry and Information Technology under Grant No.KL-2023-001the High-performance Computing Platform of BUPT。
文摘The rapid development of the Internet of Vehicles(IoVs)underscores the importance of Vehicle-to-Everything(V2X)communication for ensuring driving safety.V2X supports control systems by providing reliable and real-time information,while the control system's decisions,in turn,affect the communication topology and channel state.Depending on the coupling between communication and control,radio resource allocation(RRA)should be controlaware.However,current RRA methods often focus on optimizing communication metrics,neglecting the needs of the control system.To promote the co-design of communication and control,this paper proposes a novel RRA method that integrates both communication and control considerations.From the communication perspective,the Age of Information(AoI)is introduced to measure the freshness of packets.From the control perspective,a weighted utility function based on Time-to-Collision(TTC)and driving distance is designed,emphasizing the neighboring importance and potentially dangerous vehicles.By synthesizing these two metrics,an optimization objective minimizing weighted AoI based on TTC and driving distance is formulated.The RRA process is modeled as a partially observable Markov decision process,and a multi-agent reinforcement learning algorithm incorporating positional encoding and attention mechanisms(PAMARL)is proposed.Simulation results show that PAMARL can reduce Collision Risk(CR)with better Packet Delivery Ratio(PDR)than others.
基金supported by the Science and Technology Project of State Grid Sichuan Electric Power Company Chengdu Power Supply Company under Grant No.521904240005.
文摘This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant(VPP)networks using multi-agent reinforcement learning(MARL).As the energy landscape evolves towards greater decentralization and renewable integration,traditional optimization methods struggle to address the inherent complexities and uncertainties.Our proposed MARL framework enables adaptive,decentralized decision-making for both the distribution system operator and individual VPPs,optimizing economic efficiency while maintaining grid stability.We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay.Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods,including Stackelberg game models and model predictive control,achieving an 18.73%reduction in costs and a 22.46%increase in VPP profits.The MARL framework shows particular strength in scenarios with high renewable energy penetration,where it improves system performance by 11.95%compared with traditional methods.Furthermore,our approach demonstrates superior adaptability to unexpected events and mis-predictions,highlighting its potential for real-world implementation.
基金supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX25_0621)the Foundation of Inter-disciplinary Innovation Fund for Doctoral Students of Nanjing University of Aeronautics and Astronautics(No.KXKCXJJ202507)。
文摘Conflict resolution(CR)is a fundamental component of air traffic management,where recent progress in artificial intelligence has led to the effective application of deep reinforcement learning(DRL)techniques to enhance CR strategies.However,existing DRL models applied to CR are often limited to simple scenarios.This approach frequently leads to the neglect of the high risks associated with multiple intersections in the high-density and multi-airport system terminal area(MAS-TMA),and suffers from poor interpretability.This paper addresses the aforementioned gap by introducing an improved multi-agent DRL model that adopted to autonomous CR(AutoCR)within MAS-TMA.Specifically,dynamic weather conditions are incorporated into the state space to enhance adaptability.In the action space,the flight intent is considered and transformed into optimal maneuvers according to overload,thus improving interpretability.On these bases,the deep Q-network(DQN)algorithm is further improved to address the AutoCR problem in MAS-TMA.Simulation experiments conducted in the“Guangdong-Hong Kong-Macao”greater bay area(GBA)MAS-TMA demonstrate the effectiveness of the proposed method,successfully resolving over eight potential conflicts and performing robustly across various air traffic densities.
基金supported in part by NSFC (62102099, U22A2054, 62101594)in part by the Pearl River Talent Recruitment Program (2021QN02S643)+9 种基金Guangzhou Basic Research Program (2023A04J1699)in part by the National Research Foundation, SingaporeInfocomm Media Development Authority under its Future Communications Research Development ProgrammeDSO National Laboratories under the AI Singapore Programme under AISG Award No AISG2-RP-2020-019Energy Research Test-Bed and Industry Partnership Funding Initiative, Energy Grid (EG) 2.0 programmeDesCartes and the Campus for Research Excellence and Technological Enterprise (CREATE) programmeMOE Tier 1 under Grant RG87/22in part by the Singapore University of Technology and Design (SUTD) (SRG-ISTD-2021- 165)in part by the SUTD-ZJU IDEA Grant SUTD-ZJU (VP) 202102in part by the Ministry of Education, Singapore, through its SUTD Kickstarter Initiative (SKI 20210204)。
文摘Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.
基金co-supported by the National Natural Science Foundation of China(No.62103432)the China Postdoctoral Science Foundation(No.284881)the Young Talent fund of University Association for Science and Technology in Shaanxi,China(No.20210108)。
文摘Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was proposed.First,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision space.Second,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method.Simulation results show that the proposed model has good performance and low computational resource requirement.The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.
基金The financial support fromthe Major Science and Technology Programs inHenan Province(Grant No.241100210100)National Natural Science Foundation of China(Grant No.62102372)+3 种基金Henan Provincial Department of Science and Technology Research Project(Grant No.242102211068)Henan Provincial Department of Science and Technology Research Project(Grant No.232102210078)the Stabilization Support Program of The Shenzhen Science and Technology Innovation Commission(Grant No.20231130110921001)the Key Scientific Research Project of Higher Education Institutions of Henan Province(Grant No.24A520042)is acknowledged.
文摘Aiming at the rapid growth of network services,which leads to the problems of long service request processing time and high deployment cost in the deployment of network function virtualization service function chain(SFC)under 5G networks,this paper proposes a multi-agent deep deterministic policy gradient optimization algorithm for SFC deployment(MADDPG-SD).Initially,an optimization model is devised to enhance the request acceptance rate,minimizing the latency and deploying the cost SFC is constructed for the network resource-constrained case.Subsequently,we model the dynamic problem as a Markov decision process(MDP),facilitating adaptation to the evolving states of network resources.Finally,by allocating SFCs to different agents and adopting a collaborative deployment strategy,each agent aims to maximize the request acceptance rate or minimize latency and costs.These agents learn strategies from historical data of virtual network functions in SFCs to guide server node selection,and achieve approximately optimal SFC deployment strategies through a cooperative framework of centralized training and distributed execution.Experimental simulation results indicate that the proposed method,while simultaneously meeting performance requirements and resource capacity constraints,has effectively increased the acceptance rate of requests compared to the comparative algorithms,reducing the end-to-end latency by 4.942%and the deployment cost by 8.045%.
基金partially supported by the National Key Research and Development Program of the Ministry of Science and Technology of China(2022YFE0114200)the National Natural Science Foundation of China(U20A6004).
文摘This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved proximal policy optimization(IPPO)method to make real-time decisions for the DHHBFSP.A multi-objective Markov decision process is modeled for the DHHBFSP,where the reward function is represented by a vector with dynamic weights instead of the common objectiverelated scalar value.A factory agent(FA)is formulated for each factory to select unscheduled jobs and is trained by the proposed IPPO to improve the decision quality.Multiple FAs work asynchronously to allocate jobs that arrive randomly at the shop.A two-stage training strategy is introduced in the IPPO,which learns from both single-and dual-policy data for better data utilization.The proposed IPPO is tested on randomly generated instances and compared with variants of the basic proximal policy optimization(PPO),dispatch rules,multi-objective metaheuristics,and multi-agent reinforcement learning methods.Extensive experimental results suggest that the proposed strategies offer significant improvements to the basic PPO,and the proposed IPPO outperforms the state-of-the-art scheduling methods in both convergence and solution quality.
基金the support of the Chinese Special Research Project for Civil Aircraft(No.MJZ17N22)the National Natural Science Foundation of China(Nos.U2133207,U2333214)+1 种基金the China Postdoctoral Science Foundation(No.2023M741687)the National Social Science Fund of China(No.22&ZD169)。
文摘Unmanned Aerial Vehicle(UAV)stands as a burgeoning electric transportation carrier,holding substantial promise for the logistics sector.A reinforcement learning framework Centralized-S Proximal Policy Optimization(C-SPPO)based on centralized decision process and considering policy entropy(S)is proposed.The proposed framework aims to plan the best scheduling scheme with the objective of minimizing both the timeout of order requests and the flight impact of UAVs that may lead to conflicts.In this framework,the intents of matching act are generated through the observations of UAV agents,and the ultimate conflict-free matching results are output under the guidance of a centralized decision maker.Concurrently,a pre-activation operation is introduced to further enhance the cooperation among UAV agents.Simulation experiments based on real-world data from New York City are conducted.The results indicate that the proposed CSPPO outperforms the baseline algorithms in the Average Delay Time(ADT),the Maximum Delay Time(MDT),the Order Delay Rate(ODR),the Average Flight Distance(AFD),and the Flight Impact Ratio(FIR).Furthermore,the framework demonstrates scalability to scenarios of different sizes without requiring additional training.
基金supported by the National Key Research and Development Program of China,Grant No.2020YFB0905900.
文摘The Virtual Power Plant(VPP),as an innovative power management architecture,achieves flexible dispatch and resource optimization of power systems by integrating distributed energy resources.However,due to significant differences in operational costs and flexibility of various types of generation resources,as well as the volatility and uncertainty of renewable energy sources(such as wind and solar power)and the complex variability of load demand,the scheduling optimization of virtual power plants has become a critical issue that needs to be addressed.To solve this,this paper proposes an intelligent scheduling method for virtual power plants based on Deep Reinforcement Learning(DRL),utilizing Deep Q-Networks(DQN)for real-time optimization scheduling of dynamic peaking unit(DPU)and stable baseload unit(SBU)in the virtual power plant.By modeling the scheduling problem as a Markov Decision Process(MDP)and designing an optimization objective function that integrates both performance and cost,the scheduling efficiency and economic performance of the virtual power plant are significantly improved.Simulation results show that,compared with traditional scheduling methods and other deep reinforcement learning algorithms,the proposed method demonstrates significant advantages in key performance indicators:response time is shortened by up to 34%,task success rate is increased by up to 46%,and costs are reduced by approximately 26%.Experimental results verify the efficiency and scalability of the method under complex load environments and the volatility of renewable energy,providing strong technical support for the intelligent scheduling of virtual power plants.
基金supported by National Natural Science Foundation of China under Grant No.62372110Fujian Provincial Natural Science of Foundation under Grants 2023J02008,2024H0009.
文摘The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.
