SOTAVerified

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 476–500 of 1718 papers

TitleStatusHype
Dynamic Handover: Throw and Catch with Bimanual Hands—0
Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guarantees—0
Dynamic Pricing in High-Speed Railways Using Multi-Agent Reinforcement Learning—0
A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles—0
Dynamic Resource Management in Integrated NOMA Terrestrial-Satellite Networks using Multi-Agent Reinforcement Learning—0
Dynamic Routing for Integrated Satellite-Terrestrial Networks: A Constrained Multi-Agent Reinforcement Learning Approach—0
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning—0
Decentralized Multi-agent Reinforcement Learning based State-of-Charge Balancing Strategy for Distributed Energy Storage System—0
Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth—0
Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach—0
EdgeAgentX: A Novel Framework for Agentic AI at the Edge in Military Communication Networks—0
Decentralized Multi-Agent Reinforcement Learning with Global State Prediction—0
A Multi-Agent Reinforcement Learning Framework for Evaluating the U.S. Ending the HIV Epidemic Plan—0
Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method—0
Batch-Augmented Multi-Agent Reinforcement Learning for Efficient Traffic Signal Optimization—0
Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty—0
Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning—0
Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances—0
Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learning—0
AdaptNet: Rethinking Sensing and Communication for a Seamless Internet of Drones Experience—0
Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network—0
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning—0
Efficient Domain Coverage for Vehicles with Second-Order Dynamics via Multi-Agent Reinforcement Learning—0
Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone Networks—0
Likelihood Quantile Networks for Coordinating 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