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 76–100 of 1718 papers

TitleStatusHype
Efficient Multi-agent Reinforcement Learning by PlanningCode1
Collaborating with Humans without Human DataCode1
ELIGN: Expectation Alignment as a Multi-Agent Intrinsic RewardCode1
Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi-Agent Reinforcement LearningCode1
Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information PrinciplesCode1
C-COMA: A CONTINUAL REINFORCEMENT LEARNING MODEL FOR DYNAMIC MULTIAGENT ENVIRONMENTSCode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V CommunicationCode1
Effective control of two-dimensional Rayleigh--Bénard convection: invariant multi-agent reinforcement learning is all you needCode1
CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement LearningCode1
ALMA: Hierarchical Learning for Composite Multi-Agent TasksCode1
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing ProblemsCode1
Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language ModelsCode1
A MARL Based Multi-Target Tracking Algorithm Under Jamming Against RadarCode1
CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy ManagementCode1
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural NetworkCode1
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team CompositionCode1
Randomized Entity-wise Factorization for Multi-Agent Reinforcement LearningCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
Simplified Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
A multi-agent reinforcement learning model of common-pool resource appropriationCode1
Context-aware Communication for Multi-agent Reinforcement LearningCode1
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam SearchCode1
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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