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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 101125 of 1718 papers

TitleStatusHype
Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learningCode1
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven ExplorationCode1
A coevolutionary approach to deep multi-agent reinforcement learningCode1
FoX: Formation-aware exploration in multi-agent reinforcement learningCode1
Learning Scalable Multi-Agent Coordination by Spatial Differentiation for Traffic Signal ControlCode1
Formal Contracts Mitigate Social Dilemmas in Multi-Agent RLCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
Effective control of two-dimensional Rayleigh--Bénard convection: invariant multi-agent reinforcement learning is all you needCode1
AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-NCode1
Context-aware Communication for Multi-agent Reinforcement LearningCode1
Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language ModelsCode1
IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal ControlCode1
An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative TasksCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Information Design in Multi-Agent Reinforcement LearningCode1
Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement LearningCode1
Collaborative Visual NavigationCode1
InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemmaCode1
Collaborating with Humans without Human DataCode1
Is Machine Learning Ready for Traffic Engineering Optimization?Code1
Game-Theoretic Multiagent Reinforcement LearningCode1
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learningCode1
A Game-Theoretic Approach to Multi-Agent Trust Region OptimizationCode1
Language Instructed Reinforcement Learning for Human-AI CoordinationCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
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Benchmark Results

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