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 191200 of 1718 papers

TitleStatusHype
Strategically Efficient Exploration in Competitive Multi-agent Reinforcement LearningCode1
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement LearningCode1
Mava: a research library for distributed multi-agent reinforcement learning in JAXCode1
Collaborative Visual NavigationCode1
A Game-Theoretic Approach to Multi-Agent Trust Region OptimizationCode1
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online AdvertisingCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement LearningCode1
Neural Auto-CurriculaCode1
Celebrating Diversity in Shared 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