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Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

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Showing 251–275 of 1718 papers

TitleStatusHype
Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement LearningCode1
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
Role Play: Learning Adaptive Role-Specific Strategies in Multi-Agent Interactions—0
Anytime-Constrained Equilibria in Polynomial Time—0
Demand-Aware Beam Hopping and Power Allocation for Load Balancing in Digital Twin empowered LEO Satellite Networks—0
Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone Networks—0
A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications—0
Toward Finding Strong Pareto Optimal Policies in Multi-Agent Reinforcement LearningCode0
Multi-Agent Reinforcement Learning with Selective State-Space Models—0
Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration—0
Evolutionary Dispersal of Ecological Species via Multi-Agent Deep Reinforcement Learning—0
PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal ControlCode1
Evolution of Societies via Reinforcement LearningCode0
Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense—0
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning—0
Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning—0
Scalable spectral representations for multi-agent reinforcement learning in network MDPs—0
A New Approach to Solving SMAC Task: Generating Decision Tree Code from Large Language ModelsCode2
FlickerFusion: Intra-trajectory Domain Generalizing Multi-Agent RL—0
A Distributed Primal-Dual Method for Constrained Multi-agent Reinforcement Learning with General Parameterization—0
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement LearningCode2
Cooperation and Fairness in Multi-Agent Reinforcement LearningCode1
Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement LearningCode1
Large Legislative Models: Towards Efficient AI Policymaking in Economic SimulationsCode0
Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games—0
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All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified