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 201225 of 1718 papers

TitleStatusHype
Lenient Multi-Agent Deep Reinforcement LearningCode1
"Other-Play" for Zero-Shot CoordinationCode1
FoX: Formation-aware exploration in multi-agent reinforcement learningCode1
Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement LearningCode1
Fully Decentralized Multi-Agent Reinforcement Learning with Networked AgentsCode1
Learning Scalable Multi-Agent Coordination by Spatial Differentiation for Traffic Signal ControlCode1
CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy ManagementCode1
Learning Individually Inferred Communication for Multi-Agent CooperationCode1
Group-Aware Coordination Graph for Multi-Agent Reinforcement LearningCode1
Graph Convolutional Value Decomposition in Multi-Agent Reinforcement LearningCode1
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power SystemsCode1
Progression Cognition Reinforcement Learning with Prioritized Experience for Multi-Vehicle PursuitCode1
PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal ControlCode1
Learning a Decentralized Multi-arm Motion PlannerCode1
Laser Learning Environment: A new environment for coordination-critical multi-agent tasksCode1
Hierarchical Multi-Agent Reinforcement Learning for Air Combat ManeuveringCode1
CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement LearningCode1
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing ProblemsCode1
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement LearningCode1
HyperMARL: Adaptive Hypernetworks for Multi-Agent RLCode1
Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement LearningCode1
Learning Multi-Agent Communication through Structured Attentive ReasoningCode1
Context-aware Communication for Multi-agent Reinforcement LearningCode1
IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal ControlCode1
Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language ModelsCode1
Show:102550
← PrevPage 9 of 69Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39Unverified