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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 1311–1320 of 1718 papers

TitleStatusHype
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning—0
Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive EnvironmentsCode0
Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning—0
Dealing with Non-Stationarity in MARL via Trust-Region Decomposition—0
Decentralized Deterministic Multi-Agent Reinforcement Learning—0
Strategic bidding in freight transport using deep reinforcement learning—0
Multi-Agent Reinforcement Learning of 3D Furniture Layout Simulation in Indoor Graphics ScenesCode1
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-LearningCode1
Quantifying the effects of environment and population diversity in multi-agent reinforcement learning—0
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents—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