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 1676–1700 of 1718 papers

TitleStatusHype
Generalization in Cooperative Multi-Agent Systems—0
General sum stochastic games with networked information flows—0
Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles—0
Generative Emergent Communication: Large Language Model is a Collective World Model—0
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning—0
Goals are Enough: Inducing AdHoc cooperation among unseen Multi-Agent systems in IMFs—0
Graph Attention-based Reinforcement Learning for Trajectory Design and Resource Assignment in Multi-UAV Assisted Communication—0
Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility—0
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters—0
Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents—0
Graph Exploration for Effective Multi-agent Q-Learning—0
Greedy-based Value Representation for Efficient Coordination in Multi-agent Reinforcement Learning—0
Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning—0
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning—0
GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management—0
Grounded Answers for Multi-agent Decision-making Problem through Generative World Model—0
GTDE: Grouped Training with Decentralized Execution for Multi-agent Actor-Critic—0
H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic—0
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games—0
Harmonia: A Multi-Agent Reinforcement Learning Approach to Data Placement and Migration in Hybrid Storage Systems—0
HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism—0
Heterogeneous-Agent Mirror Learning: A Continuum of Solutions to Cooperative MARL—0
Heterogeneous Multi-Agent Reinforcement Learning for Unknown Environment Mapping—0
Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration—0
Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs—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