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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 981–990 of 1718 papers

TitleStatusHype
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces—0
Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarms—0
Reinforcement Learning for Freeway Lane-Change Regulation via Connected Vehicles—0
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions—0
Reinforcement Learning in Non-Stationary Discrete-Time Linear-Quadratic Mean-Field Games—0
Reinforcement Learning on Dyads to Enhance Medication Adherence—0
Reinforcement Learning With Reward Machines in Stochastic Games—0
Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning—0
REMAX: Relational Representation for Multi-Agent Exploration—0
Remember and Forget Experience Replay for Multi-Agent Reinforcement Learning—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