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 5175 of 1718 papers

TitleStatusHype
Effective control of two-dimensional Rayleigh--Bénard convection: invariant multi-agent reinforcement learning is all you needCode1
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement LearningCode1
Randomized Entity-wise Factorization for Multi-Agent Reinforcement LearningCode1
DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight DeliveryCode1
Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement LearningCode1
A coevolutionary approach to deep multi-agent reinforcement learningCode1
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-LearningCode1
Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level OptimizationCode1
A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal ControlCode1
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learningCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online AdvertisingCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style TransferCode1
Curriculum Learning With Counterfactual Group Relative Policy Advantage For Multi-Agent Reinforcement LearningCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
Simplified Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement LearningCode1
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler MitigationCode1
FACMAC: Factored Multi-Agent Centralised Policy GradientsCode1
Effective Multi-Agent Deep Reinforcement Learning Control with Relative Entropy RegularizationCode1
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural NetworkCode1
Cooperation and Fairness in Multi-Agent Reinforcement LearningCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
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Benchmark Results

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