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 101–150 of 1718 papers

TitleStatusHype
Learning a Decentralized Multi-arm Motion PlannerCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
A coevolutionary approach to deep multi-agent reinforcement learningCode1
Context-aware Communication for Multi-agent Reinforcement LearningCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-LearningCode1
An Empirical Study on Google Research Football Multi-agent ScenariosCode1
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler MitigationCode1
InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemmaCode1
JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 MinutesCode1
AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-NCode1
Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V CommunicationCode1
An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative TasksCode1
Learning Zero-Shot Cooperation with Humans, Assuming Humans Are BiasedCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Effective control of two-dimensional Rayleigh--Bénard convection: invariant multi-agent reinforcement learning is all you needCode1
Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information PrinciplesCode1
IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal ControlCode1
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement LearningCode1
HyperMARL: Adaptive Hypernetworks for Multi-Agent RLCode1
Game-Theoretic Multiagent Reinforcement LearningCode1
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
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay BufferCode1
Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language ModelsCode1
Efficient Multi-agent Reinforcement Learning by PlanningCode1
IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARLCode1
Collaborating with Humans without Human DataCode1
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement LearningCode1
Hierarchical Multi-Agent Reinforcement Learning for Air Combat ManeuveringCode1
Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement LearningCode1
Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language ModelsCode1
Collaborative Visual NavigationCode1
HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario GenerationCode1
Learning Scalable Multi-Agent Coordination by Spatial Differentiation for Traffic Signal ControlCode1
CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement LearningCode1
Actor-Attention-Critic for Multi-Agent Reinforcement LearningCode1
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing ProblemsCode1
Formal Contracts Mitigate Social Dilemmas in Multi-Agent RLCode1
Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-agent Deep Reinforcement Learning ApproachCode1
C-COMA: A CONTINUAL REINFORCEMENT LEARNING MODEL FOR DYNAMIC MULTIAGENT ENVIRONMENTSCode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam SearchCode1
FoX: Formation-aware exploration in multi-agent reinforcement learningCode1
Bayesian Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy ManagementCode1
Scalable Multi-agent Reinforcement Learning Algorithm for Wireless NetworksCode1
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team CompositionCode1
CoLight: Learning Network-level Cooperation for Traffic Signal ControlCode1
Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loopCode1
Show:102550
← PrevPage 3 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