Doubly robust off-policy value evaluation for reinforcement learning
We study the problem of off-policy value evaluation in reinforcement learning (RL), where
one aims to estimate the value of a new policy based on data collected by a different policy …
one aims to estimate the value of a new policy based on data collected by a different policy …
Curator:{Self-Managing} Storage for Enterprise Clusters
I Cano, S Aiyar, V Arora, M Bhattacharyya… - … USENIX Symposium on …, 2017 - usenix.org
Modern cluster storage systems perform a variety of background tasks to improve the
performance, availability, durability, and cost-efficiency of stored data. For example, cleaners …
performance, availability, durability, and cost-efficiency of stored data. For example, cleaners …
Recurrent reinforcement learning: a hybrid approach
Successful applications of reinforcement learning in real-world problems often require
dealing with partially observable states. It is in general very challenging to construct and …
dealing with partially observable states. It is in general very challenging to construct and …
Iroko: A framework to prototype reinforcement learning for data center traffic control
F Ruffy, M Przystupa, I Beschastnikh - arXiv preprint arXiv:1812.09975, 2018 - arxiv.org
Recent networking research has identified that data-driven congestion control (CC) can be
more efficient than traditional CC in TCP. Deep reinforcement learning (RL), in particular …
more efficient than traditional CC in TCP. Deep reinforcement learning (RL), in particular …
Arena: A general evaluation platform and building toolkit for multi-agent intelligence
Learning agents that are not only capable of taking tests, but also innovating is becoming a
hot topic in AI. One of the most promising paths towards this vision is multi-agent learning …
hot topic in AI. One of the most promising paths towards this vision is multi-agent learning …
Multiple-action computational model training and operation
(57) ABSTRACT A processing unit can determine a first feature value corre sponding to a
session by operating a first network compu tational model (NCM) based part on information …
session by operating a first network compu tational model (NCM) based part on information …
Multi-model controller
A processing unit can operate a first recurrent computational model (RCM) to provide first
state information and a predicted result value. The processing unit can operating a first …
state information and a predicted result value. The processing unit can operating a first …
Application of Deep Reinforcement Learning Methods in Debt Collection
In the last few years, there is a growing interest in offline reinforcement learning (offline RL)
and in reinforcement learning (RL) in general. In this paper, we presented an example of …
and in reinforcement learning (RL) in general. In this paper, we presented an example of …
Data-driven data center traffic control
F Ruffy Varga - 2019 - open.library.ubc.ca
Recent networking research has identified that data-driven congestion control (CC) can be
more efficient than traditional CC in data centers (DCs). Deep reinforcement learning (RL) …
more efficient than traditional CC in data centers (DCs). Deep reinforcement learning (RL) …