Multi-agent Reinforcement Learning Research Articles

Multi-objective two-stage robust optimization of wind/PV/thermal power system based on meta multi-agent reinforcement learning

The integration of renewable energy into the power grid poses significant challenges for optimization and scheduling of the power system. In recent years, methods based on deep reinforcement learning have surpassed traditional methods on the high complexity and long-term decision-making of power system optimization and scheduling. However, faced with the inherent uncertainty of renewable energy generation and the different optimization objectives in power system, the deep reinforcement learning methods are unable to effectively address them. This paper proposes a method that combines meta reinforcement learning with multi-agent reinforcement learning to solve the multi-objective two-stage robust optimization of wind/PV/thermal power system. We conducts optimization and scheduling experiments on the IEEE39 bus system. The results indicate that our method not only enhances the robustness of the scheduling strategy, but also outperforms baseline methods in terms of convergence, diversity, and uniformity of the Pareto frontier.

Reinforcement learning-based anatomical maps for pancreas subregion and duct segmentation.

The pancreas is a complex abdominal organ with many anatomical variations, and therefore automated pancreas segmentation from medical images is a challengingapplication. In this paper, we present a framework for segmenting individual pancreatic subregions and the pancreatic duct from three-dimensional (3D) computed tomography (CT) images. A multiagent reinforcement learning (RL) network was used to detect landmarks of the head, neck, body, and tail of the pancreas, and landmarks along the pancreatic duct in a selected target CT image. Using the landmark detection results, an atlas of pancreases was nonrigidly registered to the target image, resulting in anatomical probability maps for the pancreatic subregions and duct. The probability maps were augmented with multilabel 3D U-Net architectures to obtain the final segmentationresults. To evaluate the performance of our proposed framework, we computed the Dice similarity coefficient (DSC) between the predicted and ground truth manual segmentations on a database of 82 CT images with manually segmented pancreatic subregions and 37 CT images with manually segmented pancreatic ducts. For the four pancreatic subregions, the mean DSC improved from 0.38, 0.44, and 0.39 with standard 3D U-Net, Attention U-Net, and shifted windowing (Swin) U-Net architectures, to 0.51, 0.47, and 0.49, respectively, when utilizing the proposed RL-based framework. For the pancreatic duct, the RL-based framework achieved a mean DSC of 0.70, significantly outperforming the standard approaches and existing methods on different datasets. The resulting accuracy of the proposed RL-based segmentation framework demonstrates an improvement against segmentation with standard U-Netarchitectures.

Bidirectional Influence and Interaction for Multiagent Reinforcement Learning

Bidirectional Influence and Interaction for Multiagent Reinforcement Learning

Safe robust multi-agent reinforcement learning with neural control barrier functions and safety attention mechanism

In this paper, a novel safe robust multi-agent reinforcement learning method integrated with decentralized robust neural control barrier functions (CBFs) and a safety attention mechanism (SAM) is proposed for the safety-critical multi-agent system (MAS). Safety is fundamental in the safety-critical MAS but can be affected by factors such as modeling errors, external unknown disturbances, and time-varying observable agents. Several appropriate measures are implemented to address these issues. First, modeling errors and external disturbances are regarded as an adversary for each agent. The agent learns a policy that is robust to disturbances created by the adversary. Accordingly, decentralized robust neural CBFs are introduced to maintain the safety of the MAS, particularly when the general handcrafted CBFs are difficult to construct. The SAM, in combination with the robust neural CBFs, provides a control policy with the capacity to handle time-varying observable agents and increases its attention to dangerous events. The online fine-tuning procedure further enhances the safety. Finally, experiments demonstrate the safety and effectiveness of the proposed method.

Matching Combined Heterogeneous Multi-Agent Reinforcement Learning for Resource Allocation in NOMA-V2X Networks

Matching Combined Heterogeneous Multi-Agent Reinforcement Learning for Resource Allocation in NOMA-V2X Networks

Distributed Safe Multi-Agent Reinforcement Learning: Joint Design of THz-Enabled UAV Trajectory and Channel Allocation

Distributed Safe Multi-Agent Reinforcement Learning: Joint Design of THz-Enabled UAV Trajectory and Channel Allocation

Quantum Multi-Agent Reinforcement Learning is All You Need: Coordinated Global Access in Integrated TN/NTN Cube-Satellite Networks

Quantum Multi-Agent Reinforcement Learning is All You Need: Coordinated Global Access in Integrated TN/NTN Cube-Satellite Networks

Decentralized Multiagent Reinforcement Learning Based State-of-Charge Balancing Strategy for Distributed Energy Storage System

Decentralized Multiagent Reinforcement Learning Based State-of-Charge Balancing Strategy for Distributed Energy Storage System

A Fault-Tolerant Multi-Agent Reinforcement Learning Framework for Unmanned Aerial Vehicles–Unmanned Ground Vehicle Coverage Path Planning

In this paper, we introduce a fault-tolerant multi-agent reinforcement learning framework called SERT-DQN to optimize the operations of UAVs with UGV central control in coverage path planning missions. Our approach leverages dual learning systems that combine individual agent autonomy with centralized strategic planning, thus enhancing the efficiency of cooperative path planning missions. This framework is designed for high performance in environments with fault uncertainty detected and operational challenges such as interruptions in connectivity and compromised sensor reliability. With the integration of an innovative communication system between agents, our system appropriately handles both static and dynamic environments. Also, we introduce similarity-based shared experience replay to attain faster convergence and sample efficiency in the multi-agent system. The architecture is specially designed to respond adaptively to such irregularities by effectively showing enhanced resilience in scenarios where data integrity is impaired due to faults or the UAV faces disruptions. Simulation results indicate that our fault tolerance algorithms are very resilient and do indeed improve mission outcomes, especially under dynamic and highly uncertain operating conditions. This approach becomes critical for the most recent sensor-based research in autonomous systems.

Impulsive maneuver strategy for multi-agent orbital pursuit-evasion game under sparse rewards

To address the subjectivity of dense reward designs for the orbital pursuit-evasion game with multiple optimization objectives, this paper proposes the reinforcement learning method with a hierarchical network structure to guide game strategies under sparse rewards. Initially, to overcome the convergence challenges in the reinforcement learning training process under sparse rewards, a hierarchical network structure is proposed based on the hindsight experience replay. Subsequently, considering the strict constraints imposed by orbital dynamics on spacecraft state space, the reachable domain method is introduced to refine the subgoal space in the hierarchical network, further facilitating the achievement of subgoals. Finally, by adopting the centralized training-layered execution approach, a complete multi-agent reinforcement learning method with the hierarchical network structure is established, enabling networks at each level to learn effectively in parallel within sparse reward environments. Numerical simulations indicate that, under the single-agent reinforcement learning framework, the proposed method exhibits superior stability in the late training stage and enhances exploration efficiency in the early stage by 38.89% to 55.56% to the baseline method. Under the multi-agent reinforcement learning framework, as the relative distance decreases, the subgoals generated by the hierarchical network transition from long-term to short-term, aligning with human behavioral logic.

Three-dimensional cooperative guidance with impact angle constraints via value-policy decomposed multi-agent reinforcement learning

For a three-dimensional impact angle-constrained time-coordination guidance problem, a value-policy decomposed multi-agent twin delayed deep deterministic (VPD-MATD3) policy gradient algorithm is proposed in this paper, and a temporal-spatial cooperative guidance law is designed consequently. The derived cooperative engagement kinematics is formulated as a value-policy decomposed partially observable Markov decision process. Value decomposition and policy decomposition are correspondingly proposed to address the two bottlenecks of reward coupling and policy coupling in 3D guidance, thus improving the training process. The training environment with multiple uncertainties, such as observation noise, maneuverability perturbation, autopilot time lag, switching communication topology, and random initialization, is subsequently constructed. Time-energy type reward functions are designed, incorporating energy consumption as a direct optimization indicator via a penalty term. A long short-term memory network layer is inserted inside the policy networks to suppress the negative effect of observation noise. The policy training curves verify the significant effect of the proposed algorithm in improving training convergence. The policy testing results illustrate the robustness and generalization of the VPD-MATD3 cooperative guidance law and its ability to cope with randomly switched communication topologies. Moreover, the comparative testing further reveals its time-energy suboptimal property.

Multi-agent reinforcement learning for task offloading with hybrid decision space in multi-access edge computing

Multi-access Edge Computing (MEC) has become a significant technology for supporting the computation-intensive and time-sensitive applications on the Internet of Things (IoT) devices. However, it is challenging to jointly optimize task offloading and resource allocation in the dynamic wireless environment with constrained edge resource. In this paper, we investigate a multi-user and multi-MEC servers system with varying task request and stochastic channel condition. Our purpose is to minimize the total energy consumption and time delay by optimizing the offloading decision, offloading ratio and computing resource allocation simultaneously. As the users are geographically distributed within an area, we formulate the problem of task offloading and resource allocation in MEC system as a partially observable Markov decision process (POMDP) and propose a novel multi-agent deep reinforcement learning (MADRL) -based algorithm to solve it. In particular, two aspects have been modified for performance enhancement: (1) To make fine-grained control, we design a novel neural network structure to effectively handle the hybrid action space arisen by the heterogeneous variables. (2) An adaptive reward mechanism is proposed to reasonably evaluate the infeasible actions and to mitigate the instability caused by manual configuration. Simulation results show the proposed method can achieve 7.12%−20.97% performance enhancements compared with the existing approaches.

An Effective Training Method for Counterfactual Multi-Agent Policy Network Based on Differential Evolution Algorithm

Due to the advantages of a centralized critic to estimate the Q-function value and decentralized actors to optimize the agents’ policies, counterfactual multi-agent (COMA) stands out in most multi-agent reinforcement learning (MARL) algorithms. The sharing of policy parameters can improve sampling efficiency and learning effectiveness, but it may lead to a lack of policy diversity. Hence, to balance parameter sharing and diversity among agents in COMA has been a persistent research topic. In this paper, an effective training method for a COMA policy network based on a differential evolution (DE) algorithm is proposed, named DE-COMA. DE-COMA introduces individuals in a population as computational units to construct the policy network with operations such as mutation, crossover, and selection. The average return of DE-COMA is set as the fitness function, and the best individual of policy network will be chosen for the next generation. By maintaining better parameter sharing to enhance parameter diversity, multi-agent strategies will become more exploratory. To validate the effectiveness of DE-COMA, experiments were conducted in the StarCraft II environment with 2s_vs_1sc, 2s3z, 3m, and 8m battle scenarios. Experimental results demonstrate that DE-COMA significantly outperforms the traditional COMA and most other multi-agent reinforcement learning algorithms in terms of win rate and convergence speed.

Deep Reinforcement Learning for Fluid Mechanics: Control, Optimization, and Automation

A comprehensive review of recent advancements in applying deep reinforcement learning (DRL) to fluid dynamics problems is presented. Applications in flow control and shape optimization, the primary fields where DRL is currently utilized, are thoroughly examined. Moreover, the review introduces emerging research trends in automation within computational fluid dynamics, a promising field for enhancing the efficiency and reliability of numerical analysis. Emphasis is placed on strategies developed to overcome challenges in applying DRL to complex, real-world engineering problems, such as data efficiency, turbulence, and partial observability. Specifically, the implementations of transfer learning, multi-agent reinforcement learning, and the partially observable Markov decision process are discussed, illustrating how these techniques can provide solutions to such issues. Finally, future research directions that could further advance the integration of DRL in fluid dynamics research are highlighted.

A multi-agent reinforcement learning based approach for intelligent traffic signal control

A multi-agent reinforcement learning based approach for intelligent traffic signal control

Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility

This paper investigates the use of deep multi-agent reinforcement learning (MARL) for the coordination of residential energy flexibility. Particularly, we focus on achieving cooperation between homes in a way that is fully privacy-preserving, scalable, and that allows for the management of distribution network voltage constraints. Previous work demonstrated that MARL-based distributed control can be achieved with no sharing of personal data required during execution. However, previous cooperative MARL-based approaches impose an ever greater training computational burden as the size of the system increases, limiting scalability. Moreover, they do not manage their impact on distribution network constraints. We therefore adopt a deep multi-agent actor–critic method that uses a centralised but factored critic to rehearse coordination ahead of execution, such that homes can successfully cooperate at scale, with only first-order growth in computational time as the system size increases. Training times are thus 34 times shorter than with a previous state-of-the-art reinforcement learning approach without the factored critic for 30 homes. Moreover, experiments show that the cooperation of agents allows for a decrease of 47.2% in the likelihood of under-voltages. The results indicate that there is significant potential value for management of energy user bills, battery depreciation, and distribution network voltage management, with minimal information and communication infrastructure requirements, no interference with daily activities, and no sharing of personal data.

Uncertainty modified policy for multi-agent reinforcement learning

Uncertainty modified policy for multi-agent reinforcement learning

A Novel Medium Access Policy Based on Reinforcement Learning in Energy-Harvesting Underwater Sensor Networks.

Underwater acoustic sensor networks (UASNs) are fundamental assets to enable discovery and utilization of sub-sea environments and have attracted both academia and industry to execute long-term underwater missions. Given the heightened significance of battery dependency in underwater wireless sensor networks, our objective is to maximize the amount of harvested energy underwater by adopting the TDMA time slot scheduling approach to prolong the operational lifetime of the sensors. In this study, we considered the spatial uncertainty of underwater ambient resources to improve the utilization of available energy and examine a stochastic model for piezoelectric energy harvesting. Considering a realistic channel and environment condition, a novel multi-agent reinforcement learning algorithm is proposed. Nodes observe and learn from their choice of transmission slots based on the available energy in the underwater medium and autonomously adapt their communication slots to their energy harvesting conditions instead of relying on the cluster head. In the numerical results, we present the impact of piezoelectric energy harvesting and harvesting awareness on three lifetime metrics. We observe that energy harvesting contributes to 4% improvement in first node dead (FND), 14% improvement in half node dead (HND), and 22% improvement in last node dead (LND). Additionally, the harvesting-aware TDMA-RL method further increases HND by 17% and LND by 38%. Our results show that the proposed method improves in-cluster communication time interval utilization and outperforms traditional time slot allocation methods in terms of throughput and energy harvesting efficiency.

Effective Patient Scheduling For Public Health Events Based On Deep Reinforcement Learning

Amidst the current worldwide pandemic of the novel coronavirus, there are still issues about the efficient allocation of emergency supplies and shortcomings in recovery protocols. To enhance individuals' health and overall welfare, it is imperative to enhance and refine the emergency patient scheduling framework for health emergencies while upholding the idea of a society with an inherent future for humanity. The paper proposed an Efficient Patient Scheduling for Public Health Emergencies (EPS-PHE) using Multiagent Reinforcement Learning (MRL). Originally intended to solve the PHE and improve EPS, the technology was conceived as a distributed multiagent system. MRL has proposed a decentralized incentive system evaluation technique for the EPS-PHE input. An incentive type called a Direct Incentive (DI) is used to show the relationships between individuals or details about two closely connected events. The quality of data transmission in EPS-PHE or the efficacy of the emergency network is measured using a Collective Incentive (CI). Simulation research has shown the value of the Deep Learning (DL) - MRL approach in EPS-PHE framework optimization.

Assessment of the broader applicability of a smart agent in peer-to-peer energy trading: A full factorial analysis of a multi-agent reinforcement learning solution

To realise the clean energy transition, peer-to-peer (P2P) renewable energy sharing markets have been proposed as one possible solution for achieving such a goal and are recognised as a potential path to achieving other goals such as affordable and reliable energy. Existing studies have shown that coordination at the micro level can be achieved by employing such P2P market structures. A pressing question concerns how to set the trade price such that the community coordinates in a way that maximises social welfare. A solution to this question based on multi-agent reinforcement learning (MARL) has been provided as a proof-of-concept in a single environment. However, various factors such as climate and community scale have been shown to affect the collective performance in such energy-sharing communities. In this work, to test the wider applicability of the proposed solution, a full factorial experiment based on the factors of climate, community scale, and price mechanism, is conducted to ascertain the response of the community w.r.t. the outputs: community self-sufficiency, total net-loss, and income equality. In short, we find that a community stands an odds of 2 to 1 in higher savings by adopting a smart agent.