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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 1061–1070 of 1718 papers

TitleStatusHype
Scaling Laws for a Multi-Agent Reinforcement Learning ModelCode0
Minimax Optimal Kernel Operator Learning via Multilevel Training—0
Macro-Action-Based Multi-Agent/Robot Deep Reinforcement Learning under Partial Observability—0
IRS Assisted NOMA Aided Mobile Edge Computing with Queue Stability: Heterogeneous Multi-Agent Reinforcement Learning—0
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning—0
Sub-optimal Policy Aided Multi-Agent Reinforcement Learning for Flocking Control—0
MA2QL: A Minimalist Approach to Fully Decentralized Multi-Agent Reinforcement Learning—0
A Robust and Constrained Multi-Agent Reinforcement Learning Electric Vehicle Rebalancing Method in AMoD Systems—0
Sample-Efficient Multi-Agent Reinforcement Learning with Demonstrations for Flocking Control—0
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified