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 526–550 of 1718 papers

TitleStatusHype
MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment—0
BenchMARL: Benchmarking Multi-Agent Reinforcement Learning—0
Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence—0
TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering ProblemCode0
Generalisable Agents for Neural Network Optimisation—0
Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play—0
Multi-Agent Reinforcement Learning for Power Control in Wireless Networks via Adaptive Graphs—0
Directly Attention Loss Adjusted Prioritized Experience Replay—0
Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach—0
Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-makingCode2
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA in the Indoor Environment via Multi-Agent Reinforcement Learning—0
Multi-agent Attacks for Black-box Social Recommendations—0
Multi-Agent Quantum Reinforcement Learning using Evolutionary OptimizationCode0
Social Motion Prediction with Cognitive Hierarchies—0
Enhancing Multi-Agent Coordination through Common Operating Picture Integration—0
Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning—0
Environmental-Impact Based Multi-Agent Reinforcement Learning—0
Kindness in Multi-Agent Reinforcement Learning—0
AI-Enabled Unmanned Vehicle-Assisted Reconfigurable Intelligent Surfaces: Deployment, Prototyping, Experiments, and Opportunities—0
Learning Independently from Causality in Multi-Agent Environments—0
RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value FactorizationCode1
Selectively Sharing Experiences Improves Multi-Agent Reinforcement LearningCode1
A Multi-Agent Reinforcement Learning Framework for Evaluating the U.S. Ending the HIV Epidemic Plan—0
QFree: A Universal Value Function Factorization for Multi-Agent Reinforcement Learning—0
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Benchmark Results

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