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 1011–1020 of 1718 papers

TitleStatusHype
RL4ReAl: Reinforcement Learning for Register Allocation—0
RLAE: Reinforcement Learning-Assisted Ensemble for LLMs—0
RMIX: Learning Risk-Sensitive Policies forCooperative Reinforcement Learning Agents—0
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents—0
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning—0
Robust Communicative Multi-Agent Reinforcement Learning with Active Defense—0
Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement Learning Approach—0
Robust Electric Vehicle Balancing of Autonomous Mobility-On-Demand System: A Multi-Agent Reinforcement Learning Approach—0
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium—0
Robust Multi-Agent Reinforcement Learning with Model Uncertainty—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