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

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Showing 801–810 of 1718 papers

TitleStatusHype
A Local Information Aggregation based Multi-Agent Reinforcement Learning for Robot Swarm Dynamic Task Allocation—0
Bidirectional Distillation: A Mixed-Play Framework for Multi-Agent Generalizable Behaviors—0
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning—0
How much can change in a year? Revisiting Evaluation in Multi-Agent Reinforcement Learning—0
How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in Urban Driving Games—0
Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learning—0
Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning—0
Curiosity-driven Exploration in Sparse-reward Multi-agent Reinforcement Learning—0
Homeostatic Coupling for Prosocial Behavior—0
CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems—0
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All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified