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 101110 of 1718 papers

TitleStatusHype
Unreal-MAP: Unreal-Engine-Based General Platform for Multi-Agent Reinforcement LearningCode3
PEnGUiN: Partially Equivariant Graph NeUral Networks for Sample Efficient MARL0
Predicting Multi-Agent Specialization via Task Parallelizability0
HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario GenerationCode1
SocialJax: An Evaluation Suite for Multi-agent Reinforcement Learning in Sequential Social DilemmasCode2
A Generalist Hanabi AgentCode0
A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives0
Towards Better Sample Efficiency in Multi-Agent Reinforcement Learning via Exploration0
LLM-Mediated Guidance of MARL Systems0
ICCO: Learning an Instruction-conditioned Coordinator for Language-guided Task-aligned Multi-robot Control0
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Benchmark Results

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