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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 276–300 of 1718 papers

TitleStatusHype
Variational Inequality Methods for Multi-Agent Reinforcement Learning: Performance and Stability Gains—0
OPTIMA: Optimized Policy for Intelligent Multi-Agent Systems Enables Coordination-Aware Autonomous VehiclesCode0
CAFEEN: A Cooperative Approach for Energy Efficient NoCs with Multi-Agent Reinforcement Learning—0
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement LearningCode1
Cooperative and Asynchronous Transformer-based Mission Planning for Heterogeneous Teams of Mobile RobotsCode0
Last Iterate Convergence in Monotone Mean Field Games—0
Learning Emergence of Interaction Patterns across Independent RL Agents in Multi-Agent Environments—0
Grounded Answers for Multi-agent Decision-making Problem through Generative World Model—0
Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance—0
ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization—0
Sable: a Performant, Efficient and Scalable Sequence Model for MARL—0
Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank—0
Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning—0
Enabling Multi-Robot Collaboration from Single-Human Guidance—0
Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training—0
Multi-agent Reinforcement Learning for Dynamic Dispatching in Material Handling Systems—0
Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement LearningCode1
Online Planning for Multi-UAV Pursuit-Evasion in Unknown Environments Using Deep Reinforcement Learning—0
PathSeeker: Exploring LLM Security Vulnerabilities with a Reinforcement Learning-Based Jailbreak Approach—0
Scalable Multi-agent Reinforcement Learning for Factory-wide Dynamic Scheduling—0
On the Hardness of Decentralized Multi-Agent Policy Evaluation under Byzantine Attacks—0
HARP: Human-Assisted Regrouping with Permutation Invariant Critic for Multi-Agent Reinforcement LearningCode0
Putting Data at the Centre of Offline Multi-Agent Reinforcement Learning—0
DCMAC: Demand-aware Customized Multi-Agent Communication via Upper Bound Training—0
Advancing Multi-Organ Disease Care: A Hierarchical Multi-Agent Reinforcement Learning Framework—0
Show:102550
← PrevPage 12 of 69Next →
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