SOTAVerified

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 501–525 of 1718 papers

TitleStatusHype
Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning—0
Empathic Coupling of Homeostatic States for Intrinsic Prosociality—0
Enforcing Cooperative Safety for Reinforcement Learning-based Mixed-Autonomy Platoon Control—0
DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach—0
Exploring Multi-Agent Reinforcement Learning for Unrelated Parallel Machine Scheduling—0
A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs—0
Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing—0
OffLight: An Offline Multi-Agent Reinforcement Learning Framework for Traffic Signal Control—0
Data-Driven Distributed Common Operational Picture from Heterogeneous Platforms using Multi-Agent Reinforcement Learning—0
Think Smart, Act SMARL! Analyzing Probabilistic Logic Shields for Multi-Agent Reinforcement LearningCode0
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
Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration—0
Multi-Agent Reinforcement Learning with Selective State-Space Models—0
Evolutionary Dispersal of Ecological Species via Multi-Agent Deep Reinforcement Learning—0
Scalable spectral representations for multi-agent reinforcement learning in network MDPs—0
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
FlickerFusion: Intra-trajectory Domain Generalizing Multi-Agent RL—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