Zobrazeno 1 - 10
of 250
pro vyhledávání: '"Shie Mannor"'
Autor:
Leena Heistrene, Ram Machlev, Michael Perl, Juri Belikov, Dmitry Baimel, Kfir Levy, Shie Mannor, Yoash Levron
Publikováno v:
Energy and AI, Vol 14, Iss , Pp 100259- (2023)
Advanced machine learning (ML) algorithms have outperformed traditional approaches in various forecasting applications, especially electricity price forecasting (EPF). However, the prediction accuracy of ML reduces substantially if the input data is
Externí odkaz:
https://doaj.org/article/11eb963bb8844d9caeb7ef96ebe7f27f
Autor:
Orly Avner, Shie Mannor
Publikováno v:
IEEE/ACM Transactions on Networking. 27:2192-2207
Communication networks shared by many users are a widespread challenge nowadays. In this paper we address several aspects of this challenge simultaneously: learning unknown stochastic network characteristics, sharing resources with other users while
Cooperative multi-agent reinforcement learning (MARL) faces significant scalability issues due to state and action spaces that are exponentially large in the number of agents. As environments grow in size, effective credit assignment becomes increasi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::862809da7e566e4ff5eaf02bc0f25f70
http://arxiv.org/abs/2109.10632
http://arxiv.org/abs/2109.10632
Publikováno v:
ACC
It is well known that highly volatile control laws, while theoretically optimal for certain systems, are undesirable from an engineering perspective, being generally deleterious to the controlled system. In this article we are concerned with the temp
Publikováno v:
AAAI
Scopus-Elsevier
Scopus-Elsevier
The Kalman filter is a key tool for time-series forecasting and analysis. We show that the dependence of a prediction of Kalman filter on the past is decaying exponentially, whenever the process noise is non-degenerate. Therefore, Kalman filter may b
Publikováno v:
IEEE Transactions on Power Systems. 34:2528-2540
Outage scheduling aims at defining, over a horizon of several months to years, when different components needing maintenance should be taken out of operation. Its objective is to minimize operation-cost expectation while satisfying reliability-relate
Publikováno v:
Robotics: Science and Systems
When transferring a control policy from simulation to a physical system, the policy needs to be robust to variations in the dynamics to perform well. Commonly, the optimal policy overfits to the approximate model and the corresponding state-distribut
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5de45951b44f926f4eaa1514572e26d2
Solving the Hamilton-Jacobi-Bellman equation is important in many domains including control, robotics and economics. Especially for continuous control, solving this differential equation and its extension the Hamilton-Jacobi-Isaacs equation, is impor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dc2dbb05aeabe501dc78de337701ac30
In Apprenticeship Learning (AL), we are given a Markov Decision Process (MDP) without access to the cost function. Instead, we observe trajectories sampled by an expert that acts according to some policy. The goal is to find a policy that matches the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb7cbf20274ad8006bedd3db901e1ef3
Autor:
Chen Tessler, Yuval Shpigelman, Gal Dalal, Amit Mandelbaum, Doron Haritan Kazakov, Benjamin Fuhrer, Gal Chechik, Shie Mannor
We approach the task of network congestion control in datacenters using Reinforcement Learning (RL). Successful congestion control algorithms can dramatically improve latency and overall network throughput. Until today, no such learning-based algorit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f8380d7cc8c18eb383a7f65ade54b1ad