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 201–250 of 1718 papers

TitleStatusHype
PyTAG: Tabletop Games for Multi-Agent Reinforcement LearningCode1
Formal Contracts Mitigate Social Dilemmas in Multi-Agent RLCode1
marl-jax: Multi-Agent Reinforcement Leaning FrameworkCode1
QPLEX: Duplex Dueling Multi-Agent Q-LearningCode1
Graph Convolutional Value Decomposition in Multi-Agent Reinforcement LearningCode1
Group-Aware Coordination Graph for Multi-Agent Reinforcement LearningCode1
Mean Field Multi-Agent Reinforcement LearningCode1
Learning Zero-Shot Cooperation with Humans, Assuming Humans Are BiasedCode1
Reinforced Prompt Personalization for Recommendation with Large Language ModelsCode1
Learning to Simulate Self-Driven Particles System with Coordinated Policy OptimizationCode1
Lenient Multi-Agent Deep Reinforcement LearningCode1
Hierarchical Multi-Agent Reinforcement Learning for Air Combat ManeuveringCode1
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement LearningCode1
HyperMARL: Adaptive Hypernetworks for Multi-Agent RLCode1
An Empirical Study on Google Research Football Multi-agent ScenariosCode1
IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal ControlCode1
CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement LearningCode1
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing ProblemsCode1
Learning to Model Opponent LearningCode1
Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement LearningCode1
E(3)-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement LearningCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Learning to Share in Multi-Agent Reinforcement LearningCode1
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement LearningCode1
Interaction Pattern Disentangling for Multi-Agent Reinforcement LearningCode1
CoLight: Learning Network-level Cooperation for Traffic Signal ControlCode1
An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative TasksCode1
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement LearningCode1
C-COMA: A CONTINUAL REINFORCEMENT LEARNING MODEL FOR DYNAMIC MULTIAGENT ENVIRONMENTSCode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?Code1
Is Machine Learning Ready for Traffic Engineering Optimization?Code1
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement LearningCode1
Scalable Reinforcement Learning Policies for Multi-Agent ControlCode1
Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement LearningCode1
Language Instructed Reinforcement Learning for Human-AI CoordinationCode1
Laser Learning Environment: A new environment for coordination-critical multi-agent tasksCode1
Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement LearningCode1
A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal ControlCode1
Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement LearningCode1
Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language ModelsCode1
Collaborating with Humans without Human DataCode1
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team CompositionCode1
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement LearningCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learningCode1
A Game-Theoretic Approach to Multi-Agent Trust Region OptimizationCode1
Learning Individually Inferred Communication for Multi-Agent CooperationCode1
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement LearningCode1
Collaborative Visual NavigationCode1
Show:102550
← PrevPage 5 of 35Next →
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