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
Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial ExplorationCode1
Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information PrinciplesCode1
AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-NCode1
Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement LearningCode1
FoX: Formation-aware exploration in multi-agent reinforcement learningCode1
Fully Decentralized Multi-Agent Reinforcement Learning with Networked AgentsCode1
Context-aware Communication for Multi-agent Reinforcement LearningCode1
Graph Convolutional Value Decomposition in Multi-Agent Reinforcement LearningCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Collaborative Visual NavigationCode1
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Benchmark Results

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