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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 801–810 of 1718 papers

TitleStatusHype
Uncoupled Learning of Differential Stackelberg Equilibria with CommitmentsCode0
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning—0
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning—0
Offline Learning in Markov Games with General Function Approximation—0
Dual Self-Awareness Value Decomposition Framework without Individual Global Max for Cooperative Multi-Agent Reinforcement Learning—0
Learning Zero-Shot Cooperation with Humans, Assuming Humans Are BiasedCode1
Best Possible Q-Learning—0
Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement LearningCode2
Learning Roles with Emergent Social Value Orientations—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