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 476500 of 1718 papers

TitleStatusHype
GOV-REK: Governed Reward Engineering Kernels for Designing Robust Multi-Agent Reinforcement Learning SystemsCode0
Heterogeneous Multi-Agent Reinforcement Learning via Mirror Descent Policy OptimizationCode0
Fully Independent Communication in Multi-Agent Reinforcement LearningCode0
Arena: a toolkit for Multi-Agent Reinforcement LearningCode0
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent IntelligenceCode0
Generalising Multi-Agent Cooperation through Task-Agnostic CommunicationCode0
FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical ImagingCode0
Finding Friend and Foe in Multi-Agent GamesCode0
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement LearningCode0
An Organizationally-Oriented Approach to Enhancing Explainability and Control in Multi-Agent Reinforcement LearningCode0
Extended Markov Games to Learn Multiple Tasks in Multi-Agent Reinforcement LearningCode0
Generalizable Agent Modeling for Agent Collaboration-Competition Adaptation with Multi-Retrieval and Dynamic GenerationCode0
Heterogeneous Multi-agent Zero-Shot Coordination by CoevolutionCode0
A Regularized Opponent Model with Maximum Entropy ObjectiveCode0
Evolution of Societies via Reinforcement LearningCode0
eQMARL: Entangled Quantum Multi-Agent Reinforcement Learning for Distributed Cooperation over Quantum ChannelsCode0
Effects of Spectral Normalization in Multi-agent Reinforcement LearningCode0
Multi-agent reinforcement learning for the control of three-dimensional Rayleigh-Bénard convectionCode0
Learning to Play General-Sum Games Against Multiple Boundedly Rational AgentsCode0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement LearningCode0
A Generalist Hanabi AgentCode0
CoMIX: A Multi-agent Reinforcement Learning Training Architecture for Efficient Decentralized Coordination and Independent Decision-MakingCode0
Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention MechanismsCode0
Enhancing Language Multi-Agent Learning with Multi-Agent Credit Re-Assignment for Interactive Environment GeneralizationCode0
Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning AlgorithmsCode0
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Benchmark Results

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