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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 1351–1360 of 1718 papers

TitleStatusHype
Neural Recursive Belief States in Multi-Agent Reinforcement Learning—0
Neuron as an Agent—0
Never Explore Repeatedly in Multi-Agent Reinforcement Learning—0
Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning—0
Non-Autoregressive Image Captioning with Counterfactuals-Critical Multi-Agent Learning—0
Non-Linear Coordination Graphs—0
NQMIX: Non-monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning—0
Non-Stationary Policy Learning for Multi-Timescale Multi-Agent Reinforcement Learning—0
Off-Beat Multi-Agent Reinforcement Learning—0
OffLight: An Offline Multi-Agent Reinforcement Learning Framework for Traffic Signal Control—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