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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 781–790 of 1718 papers

TitleStatusHype
CURO: Curriculum Learning for Relative Overgeneralization—0
Autonomous Vehicle Patrolling Through Deep Reinforcement Learning: Learning to Communicate and Cooperate—0
Human-Machine Dialogue as a Stochastic Game—0
Human Machine Co-adaption Interface via Cooperation Markov Decision Process System—0
Kindness in Multi-Agent Reinforcement Learning—0
Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles—0
Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm—0
KnowSR: Knowledge Sharing among Homogeneous Agents in Multi-agent Reinforcement Learning—0
Human and Multi-Agent collaboration in a human-MARL teaming framework—0
Curriculum Learning for Cooperation in 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