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 151175 of 1718 papers

TitleStatusHype
C-COMA: A CONTINUAL REINFORCEMENT LEARNING MODEL FOR DYNAMIC MULTIAGENT ENVIRONMENTSCode1
Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy OptimizationCode1
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?Code1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
Cooperation and Fairness in Multi-Agent Reinforcement LearningCode1
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
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement LearningCode1
Hierarchical Multi-Agent Reinforcement Learning for Air Combat ManeuveringCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
Randomized Entity-wise Factorization for Multi-Agent Reinforcement LearningCode1
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative ExplorationCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Curriculum Learning With Counterfactual Group Relative Policy Advantage For Multi-Agent Reinforcement LearningCode1
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler MitigationCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level OptimizationCode1
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement LearningCode1
DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight DeliveryCode1
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement LearningCode1
Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement LearningCode1
ALMA: Hierarchical Learning for Composite Multi-Agent TasksCode1
Learning to Model Opponent LearningCode1
Graph Convolutional Value Decomposition in Multi-Agent Reinforcement LearningCode1
Show:102550
← PrevPage 7 of 69Next →

Benchmark Results

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