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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
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach—0
Learning Emergence of Interaction Patterns across Independent RL Agents in Multi-Agent Environments—0
LQR with Tracking: A Zeroth-order Approach and Its Global Convergence—0
Federated Learning for Distributed Energy-Efficient Resource Allocation—0
Kindness in Multi-Agent Reinforcement Learning—0
Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles—0
Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming—0
KnowSR: Knowledge Sharing among Homogeneous Agents in Multi-agent Reinforcement Learning—0
LAMARL: LLM-Aided Multi-Agent Reinforcement Learning for Cooperative Policy Generation—0
Federated Hierarchical Reinforcement Learning for Adaptive 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