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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

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Showing 51–60 of 1718 papers

TitleStatusHype
Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial ExplorationCode1
A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal ControlCode1
HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario GenerationCode1
Trajectory-Class-Aware Multi-Agent Reinforcement LearningCode1
Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement LearningCode1
RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehiclesCode1
Training Language Models for Social Deduction with Multi-Agent Reinforcement LearningCode1
An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative TasksCode1
Scalable Safe Multi-Agent Reinforcement Learning for Multi-Agent SystemCode1
WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm ControlCode1
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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