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 561–570 of 1718 papers

TitleStatusHype
DSDF: Coordinated look-ahead strategy in stochastic multi-agent reinforcement learning—0
Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control—0
DSDF: An approach to handle stochastic agents in collaborative multi-agent reinforcement learning—0
Centralized vs. Decentralized Multi-Agent Reinforcement Learning for Enhanced Control of Electric Vehicle Charging Networks—0
An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility—0
A finite time analysis of distributed Q-learning—0
Double Distillation Network for Multi-Agent Reinforcement Learning—0
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients—0
An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning—0
DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach—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