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 1281–1290 of 1718 papers

TitleStatusHype
An approach to implement Reinforcement Learning for Heterogeneous Vehicular Networks—0
An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control—0
A Neuro-Symbolic Approach to Multi-Agent RL for Interpretability and Probabilistic Decision Making—0
A New Framework for Multi-Agent Reinforcement Learning -- Centralized Training and Exploration with Decentralized Execution via Policy Distillation—0
A New Policy Iteration Algorithm For Reinforcement Learning in Zero-Sum Markov Games—0
Satisficing Paths and Independent Multi-Agent Reinforcement Learning in Stochastic Games—0
An In-Depth Analysis of Discretization Methods for Communication Learning using Backpropagation with Multi-Agent Reinforcement Learning—0
An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning—0
An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility—0
An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management—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