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

Showing 126150 of 1718 papers

TitleStatusHype
Mava: a research library for distributed multi-agent reinforcement learning in JAXCode1
Scalable Multi-agent Reinforcement Learning Algorithm for Wireless NetworksCode1
FACMAC: Factored Multi-Agent Centralised Policy GradientsCode1
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement LearningCode1
Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement LearningCode1
DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight DeliveryCode1
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-LearningCode1
Effective control of two-dimensional Rayleigh--Bénard convection: invariant multi-agent reinforcement learning is all you needCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
Cooperation and Fairness in Multi-Agent Reinforcement LearningCode1
Actor-Attention-Critic for Multi-Agent Reinforcement LearningCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement LearningCode1
Context-aware Communication for Multi-agent Reinforcement LearningCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Cooperative Policy Learning with Pre-trained Heterogeneous Observation RepresentationsCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Curriculum Learning With Counterfactual Group Relative Policy Advantage For Multi-Agent Reinforcement LearningCode1
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler MitigationCode1
Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement LearningCode1
Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level OptimizationCode1
Collaborating with Humans without Human DataCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy OptimizationCode1
Collaborative Visual NavigationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39Unverified