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 501–550 of 1718 papers

TitleStatusHype
ColorGrid: A Multi-Agent Non-Stationary Environment for Goal Inference and AssistanceCode0
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed AgentsCode0
Emergent Dominance Hierarchies in Reinforcement Learning AgentsCode0
FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical ImagingCode0
Fully Independent Communication in Multi-Agent Reinforcement LearningCode0
GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement LearningCode0
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement LearningCode0
Evolution of Societies via Reinforcement LearningCode0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement LearningCode0
eQMARL: Entangled Quantum Multi-Agent Reinforcement Learning for Distributed Cooperation over Quantum ChannelsCode0
Learning to Play General-Sum Games Against Multiple Boundedly Rational AgentsCode0
Extended Markov Games to Learn Multiple Tasks in Multi-Agent Reinforcement LearningCode0
Gifting in multi-agent reinforcement learningCode0
Learning Complex Teamwork Tasks Using a Given Sub-task DecompositionCode0
PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement LearningCode0
Efficient Training in Multi-Agent Reinforcement Learning: A Communication-Free Framework for the Box-Pushing Problem—0
Efficient Replay Memory Architectures in Multi-Agent Reinforcement Learning for Traffic Congestion Control—0
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework—0
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning—0
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation—0
Efficiently Quantifying Individual Agent Importance in Cooperative MARL—0
Coding for Distributed Multi-Agent Reinforcement Learning—0
An Initial Introduction to Cooperative Multi-Agent Reinforcement Learning—0
Efficiently Computing Nash Equilibria in Adversarial Team Markov Games—0
AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning—0
Efficient Domain Coverage for Vehicles with Second-Order Dynamics via Multi-Agent Reinforcement Learning—0
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning—0
ClusterComm: Discrete Communication in Decentralized MARL using Internal Representation Clustering—0
Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network—0
Efficient Communication via Self-supervised Information Aggregation for Online and Offline Multi-agent Reinforcement Learning—0
Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks—0
Anytime PSRO for Two-Player Zero-Sum Games—0
Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach—0
Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learning—0
Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning—0
Closure Discovery for Coarse-Grained Partial Differential Equations Using Grid-based Reinforcement Learning—0
EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge—0
CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning—0
Anytime-Constrained Equilibria in Polynomial Time—0
EdgeAgentX: A Novel Framework for Agentic AI at the Edge in Military Communication Networks—0
Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach—0
Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth—0
Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning—0
Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning—0
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning—0
Dynamic Routing for Integrated Satellite-Terrestrial Networks: A Constrained Multi-Agent Reinforcement Learning Approach—0
Characterizing Speed Performance of Multi-Agent Reinforcement Learning—0
Dynamic Resource Management in Integrated NOMA Terrestrial-Satellite Networks using Multi-Agent Reinforcement Learning—0
Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning—0
Decentralized Multi-Agents by Imitation of a Centralized Controller—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