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Found 7 papers, 2 papers with code
Collaborative Optimization of the Age of Information under Partial Observability
In this work, we devise a decentralized AoI-minimizing transmission policy for a number of sensor agents sharing capacity-limited, non-FIFO duplex channels that introduce random delays in communication with a common receiver.
Sparse Mean Field Load Balancing in Large Localized Queueing Systems
Empirically, the proposed methodology performs well on several realistic and scalable wireless network topologies as compared to a number of well-known load balancing heuristics and existing scalable multi-agent reinforcement learning methods.
A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning
The analysis and control of large-population systems is of great interest to diverse areas of research and engineering, ranging from epidemiology over robotic swarms to economics and finance.
Decentralized Coordination in Partially Observable Queueing Networks
We consider communication in a fully cooperative multi-agent system, where the agents have partial observation of the environment and must act jointly to maximize the overall reward.
Learning Mean-Field Control for Delayed Information Load Balancing in Large Queuing Systems
In this work, we consider a multi-agent load balancing system, with delayed information, consisting of many clients (load balancers) and many parallel queues.
Load Balancing in Compute Clusters with Delayed Feedback
In this paper, we provide a partially observable (PO) model that captures the load balancing decisions in parallel buffered systems under limited information of delayed acknowledgements.
Discrete-Time Mean Field Control with Environment States
Multi-agent reinforcement learning methods have shown remarkable potential in solving complex multi-agent problems but mostly lack theoretical guarantees.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
