Distributed workload queues are nowadays widely used due to their significant advantages in terms of decoupling, resilience, and scaling. Task allocation worker nodes distributed queue systems is typically simplistic (e.g., Least Recently Used) or uses hand-crafted heuristics that require task-specific information task resource demands expected time execution). When such not available node capa...

Mobile edge computing (MEC) is a prominent paradigm which expands the application fields of wireless communication. Due to limitation capacities user equipments and MEC servers, caching (EC) optimization crucial effective utilization resources in MEC-enabled networks. However, dynamics complexities content popularities over space time as well privacy preservation users pose significant challeng...

This article deals with distributed policy optimization in reinforcement learning, which involves a central controller and group of learners. In particular, two typical settings encountered several applications are considered: multiagent learning (RL) xmlns:xlink="http://www.w3.org/1999/xlink">...

This Dagstuhl Seminar also stood as the 11th European Workshop on Reinforcement Learning (EWRL11). Reinforcement learning gains more and more attention each year, as can be seen at the various conferences (ECML, ICML, IJCAI, . . . ). EWRL, and in particular this Dagstuhl Seminar, aimed at gathering people interested in reinforcement learning from all around the globe. This unusual format for EW...

We consider the problem of designing distributed controllers to stabilize a class networked systems, where each subsystem is dissipative and designs reinforcement learning based local controller maximize an individual cumulative reward function. develop approach that enforces dissipativity conditions on these at guarantee stability entire system. The proposed illustrated DC microgrid example, o...

In this article, we propose several novel distributed gradient-based temporal-difference algorithms for multiagent off-policy learning of linear approximation the value function in Markov decision processes with strict information structure constraints, limiting interagent communications to small neighborhoods. The are composed following: first, local parameter updates based on single-agent gra...

This Dagstuhl Seminar also stood as the 11th European Workshop on Reinforcement Learning (EWRL11). Reinforcement learning gains more and more attention each year, as can be seen at the various conferences (ECML, ICML, IJCAI, . . . ). EWRL, and in particular this Dagstuhl Seminar, aimed at gathering people interested in reinforcement learning from all around the globe. This unusual format for EW...