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

Showing 126–150 of 1718 papers

TitleStatusHype
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
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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