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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 1181–1190 of 1718 papers

TitleStatusHype
UNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecution—0
Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Control—0
Universal Agent Mixtures and the Geometry of Intelligence—0
Multi-agent Attacks for Black-box Social Recommendations—0
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers—0
Using a single actor to output personalized policy for different intersections—0
Dynamic Queue-Jump Lane for Emergency Vehicles under Partially Connected Settings: A Multi-Agent Deep Reinforcement Learning Approach—0
Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training—0
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning—0
Value Variance Minimization for Learning Approximate Equilibrium in Aggregation Systems—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