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 826–850 of 1718 papers

TitleStatusHype
LearningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement Learning—0
Learning Homophilic Incentives in Sequential Social Dilemmas—0
Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functions—0
Fast Sequence Generation with Multi-Agent Reinforcement Learning—0
Fast Multi-Agent Temporal-Difference Learning via Homotopy Stochastic Primal-Dual Optimization—0
Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games—0
Fairness in Multi-agent Reinforcement Learning for Stock Trading—0
Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach—0
Learning Meta Representations for Agents in Multi-Agent Reinforcement Learning—0
Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing—0
Control as Probabilistic Inference as an Emergent Communication Mechanism in Multi-Agent Reinforcement Learning—0
Learning Multi-agent Multi-machine Tending by Mobile Robots—0
Learning Multiple Coordinated Agents under Directed Acyclic Graph Constraints—0
Learning Multi-Robot Coordination through Locality-Based Factorized Multi-Agent Actor-Critic Algorithm—0
Learning Nash Equilibria in Zero-Sum Stochastic Games via Entropy-Regularized Policy Approximation—0
Agent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learning—0
Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning—0
Learning to Transfer Role Assignment Across Team Sizes—0
A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems—0
Learning Roles with Emergent Social Value Orientations—0
Dialogue Management based on Multi-domain Corpus—0
Learning Structured Communication for Multi-agent Reinforcement Learning—0
Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning—0
Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas—0
Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approach—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