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 1526–1550 of 1718 papers

TitleStatusHype
Distributed off-Policy Actor-Critic Reinforcement Learning with Policy Consensus—0
Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning—0
Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies—0
Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach—0
Distributed Reinforcement Learning for Robot Teams: A Review—0
Distributed Traffic Control in Complex Dynamic Roadblocks: A Multi-Agent Deep RL Approach—0
Distributed Value Decomposition Networks with Networked Agents—0
Distributed Value Function Approximation for Collaborative Multi-Agent Reinforcement Learning—0
Distributional Black-Box Model Inversion Attack with Multi-Agent Reinforcement Learning—0
Distributionally Robust Multi-Agent Reinforcement Learning for Dynamic Chute Mapping—0
Divergence-Regularized Multi-Agent Actor-Critic—0
Diverse Conventions for Human-AI Collaboration—0
DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach—0
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients—0
Double Distillation Network for Multi-Agent Reinforcement Learning—0
DSDF: An approach to handle stochastic agents in collaborative multi-agent reinforcement learning—0
DSDF: Coordinated look-ahead strategy in stochastic multi-agent reinforcement learning—0
Dual Self-Awareness Value Decomposition Framework without Individual Global Max for Cooperative Multi-Agent Reinforcement Learning—0
Dynamic Collaborative Multi-Agent Reinforcement Learning Communication for Autonomous Drone Reforestation—0
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
Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning—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
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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