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

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Showing 501–525 of 1718 papers

TitleStatusHype
Finding Friend and Foe in Multi-Agent GamesCode0
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed AgentsCode0
Emergent Dominance Hierarchies in Reinforcement Learning AgentsCode0
FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical ImagingCode0
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement LearningCode0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement LearningCode0
Explainable Multi-Agent Reinforcement Learning for Temporal QueriesCode0
Extended Markov Games to Learn Multiple Tasks in Multi-Agent Reinforcement LearningCode0
eQMARL: Entangled Quantum Multi-Agent Reinforcement Learning for Distributed Cooperation over Quantum ChannelsCode0
Learning to Play General-Sum Games Against Multiple Boundedly Rational AgentsCode0
Enhancing Language Multi-Agent Learning with Multi-Agent Credit Re-Assignment for Interactive Environment GeneralizationCode0
Evolution of Societies via Reinforcement LearningCode0
Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement LearningCode0
Learning to Share and Hide Intentions using Information RegularizationCode0
On Centralized Critics in Multi-Agent Reinforcement LearningCode0
Efficient Training in Multi-Agent Reinforcement Learning: A Communication-Free Framework for the Box-Pushing Problem—0
Efficient Replay Memory Architectures in Multi-Agent Reinforcement Learning for Traffic Congestion Control—0
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework—0
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning—0
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation—0
Efficiently Quantifying Individual Agent Importance in Cooperative MARL—0
Coding for Distributed Multi-Agent Reinforcement Learning—0
An Initial Introduction to Cooperative Multi-Agent Reinforcement Learning—0
Efficiently Computing Nash Equilibria in Adversarial Team Markov Games—0
AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning—0
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All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified