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 1581–1590 of 1718 papers

TitleStatusHype
A Review of Cooperative Multi-Agent Deep Reinforcement Learning—0
Large-Scale Traffic Signal Control Using a Novel Multi-Agent Reinforcement Learning—0
Fast Multi-Agent Temporal-Difference Learning via Homotopy Stochastic Primal-Dual Optimization—0
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning—0
Health-Informed Policy Gradients for Multi-Agent Reinforcement LearningCode0
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition—0
Finite-Time Performance of Distributed Temporal Difference Learning with Linear Function Approximation—0
Arena: a toolkit for Multi-Agent Reinforcement LearningCode0
Prioritized Guidance for Efficient Multi-Agent Reinforcement Learning Exploration—0
Shapley Q-value: A Local Reward Approach to Solve Global Reward GamesCode0
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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