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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 731–740 of 1718 papers

TitleStatusHype
Group-Agent Reinforcement Learning—0
Independent Natural Policy Gradient Always Converges in Markov Potential Games—0
Improving Global Parameter-sharing in Physically Heterogeneous Multi-agent Reinforcement Learning with Unified Action Space—0
Independent Policy Mirror Descent for Markov Potential Games: Scaling to Large Number of Players—0
Improved cooperation by balancing exploration and exploitation in intertemporal social dilemma tasks—0
DCMAC: Demand-aware Customized Multi-Agent Communication via Upper Bound Training—0
Impression Allocation and Policy Search in Display Advertising—0
Inducing Cooperation via Learning to reshape rewards in semi-cooperative multi-agent reinforcement learning—0
Implementations that Matter in Cooperative Multi-Agent Reinforcement Learning—0
DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning—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