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NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL

NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL

5 October 2021
Khaled Nakhleh
Santosh Ganji
Ping-Chun Hsieh
I.-Hong Hou
S. Shakkottai
ArXivPDFHTML

Papers citing "NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL"

15 / 15 papers shown
Title
The Bandit Whisperer: Communication Learning for Restless Bandits
The Bandit Whisperer: Communication Learning for Restless Bandits
Yunfan Zhao
Tonghan Wang
Dheeraj M. Nagaraj
Aparna Taneja
Milind Tambe
73
5
0
11 Aug 2024
Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in
  Application to Preventive Healthcare
Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare
Arpita Biswas
Gaurav Aggarwal
Pradeep Varakantham
Milind Tambe
28
41
0
17 May 2021
MOPO: Model-based Offline Policy Optimization
MOPO: Model-based Offline Policy Optimization
Tianhe Yu
G. Thomas
Lantao Yu
Stefano Ermon
James Zou
Sergey Levine
Chelsea Finn
Tengyu Ma
OffRL
58
759
0
27 May 2020
MOReL : Model-Based Offline Reinforcement Learning
MOReL : Model-Based Offline Reinforcement Learning
Rahul Kidambi
Aravind Rajeswaran
Praneeth Netrapalli
Thorsten Joachims
OffRL
61
662
0
12 May 2020
Whittle index based Q-learning for restless bandits with average reward
Whittle index based Q-learning for restless bandits with average reward
Konstantin Avrachenkov
Vivek Borkar
26
69
0
29 Apr 2020
Q-Learning in enormous action spaces via amortized approximate
  maximization
Q-Learning in enormous action spaces via amortized approximate maximization
T. Wiele
David Warde-Farley
A. Mnih
Volodymyr Mnih
36
60
0
22 Jan 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke
Sam Gross
Francisco Massa
Adam Lerer
James Bradbury
...
Sasank Chilamkurthy
Benoit Steiner
Lu Fang
Junjie Bai
Soumith Chintala
ODL
181
42,038
0
03 Dec 2019
Problem Dependent Reinforcement Learning Bounds Which Can Identify
  Bandit Structure in MDPs
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs
Andrea Zanette
Emma Brunskill
76
15
0
03 Nov 2019
Recovering Bandits
Recovering Bandits
Ciara Pike-Burke
Steffen Grunewalder
108
40
0
31 Oct 2019
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep
  Networks for Thompson Sampling
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
C. Riquelme
George Tucker
Jasper Snoek
BDL
55
365
0
26 Feb 2018
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement
  Learning
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning
Christoph Dann
Tor Lattimore
Emma Brunskill
52
307
0
22 Mar 2017
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable
Nan Jiang
A. Krishnamurthy
Alekh Agarwal
John Langford
Robert Schapire
71
417
0
29 Oct 2016
Deep Reinforcement Learning in Large Discrete Action Spaces
Deep Reinforcement Learning in Large Discrete Action Spaces
Gabriel Dulac-Arnold
Richard Evans
H. V. Hasselt
P. Sunehag
Timothy Lillicrap
Jonathan J. Hunt
Timothy A. Mann
T. Weber
T. Degris
Ben Coppin
OffRL
52
572
0
24 Dec 2015
When are Kalman-filter restless bandits indexable?
When are Kalman-filter restless bandits indexable?
C. Dance
T. Silander
17
12
0
15 Sep 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
626
149,474
0
22 Dec 2014
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