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 1211–1220 of 1718 papers

TitleStatusHype
SrSv: Integrating Sequential Rollouts with Sequential Value Estimation for Multi-agent Reinforcement Learning—0
Bidirectional Distillation: A Mixed-Play Framework for Multi-Agent Generalizable Behaviors—0
Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics—0
Signal attenuation enables scalable decentralized multi-agent reinforcement learning over networks—0
Ego-centric Learning of Communicative World Models for Autonomous Driving—0
A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems—0
A Better Baseline for Second Order Gradient Estimation in Stochastic Computation Graphs—0
A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning—0
Abstracting Geo-specific Terrains to Scale Up Reinforcement Learning—0
AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning—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