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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1076–1100 of 1718 papers

TitleStatusHype
A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning—0
Learning Sparse Graphon Mean Field GamesCode0
On the Near-Optimality of Local Policies in Large Cooperative Multi-Agent Reinforcement Learning—0
Energy Management of Multi-mode Hybrid Electric Vehicles based on Hand-shaking Multi-agent Learning—0
Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning—0
Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction—0
A collaboration of multi-agent model using an interactive interfaceCode0
A further exploration of deep Multi-Agent Reinforcement Learning with Hybrid Action Space—0
Reinforcement Learning based Multi-connectivity Resource Allocation in Factory Automation Systems—0
An approach to implement Reinforcement Learning for Heterogeneous Vehicular Networks—0
CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning—0
Quantum Multi-Agent Meta Reinforcement Learning—0
Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model—0
Last-Iterate Convergence with Full and Noisy Feedback in Two-Player Zero-Sum GamesCode0
Forecasting Evolution of Clusters in Game Agents with Hebbian Learning—0
Multi-Agent Reinforcement Learning with Graph Convolutional Neural Networks for optimal Bidding Strategies of Generation Units in Electricity Markets—0
Heterogeneous Multi-agent Zero-Shot Coordination by CoevolutionCode0
Multi-agent reinforcement learning for intent-based service assurance in cellular networks—0
Learning to Coordinate for a Worker-Station Multi-robot System in Planar Coverage Tasks—0
Transferable Multi-Agent Reinforcement Learning with Dynamic Participating Agents—0
Efficiently Computing Nash Equilibria in Adversarial Team Markov Games—0
Heterogeneous-Agent Mirror Learning: A Continuum of Solutions to Cooperative MARL—0
Multi-Agent Reinforcement Learning for Long-Term Network Resource Allocation through Auction: a V2X Application—0
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks—0
Cooperative Actor-Critic via TD Error Aggregation—0
Show:102550
← PrevPage 44 of 69Next →
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