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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 321–330 of 1718 papers

TitleStatusHype
Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility—0
Center of Gravity-Guided Focusing Influence Mechanism for Multi-Agent Reinforcement Learning—0
An In-Depth Analysis of Discretization Methods for Communication Learning using Backpropagation with Multi-Agent Reinforcement Learning—0
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning—0
Decentralized Deep Reinforcement Learning for Network Level Traffic Signal Control—0
Satisficing Paths and Independent Multi-Agent Reinforcement Learning in Stochastic Games—0
CCL: Collaborative Curriculum Learning for Sparse-Reward Multi-Agent Reinforcement Learning via Co-evolutionary Task Evolution—0
Causal Multi-Agent Reinforcement Learning: Review and Open Problems—0
Adversarial attacks in consensus-based multi-agent reinforcement learning—0
Causal Mean Field 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