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Deep Exploration via Randomized Value Functions

Deep Exploration via Randomized Value Functions

22 March 2017
Ian Osband
Benjamin Van Roy
Daniel Russo
Zheng Wen
ArXivPDFHTML

Papers citing "Deep Exploration via Randomized Value Functions"

24 / 74 papers shown
Title
Model-based Reinforcement Learning for Continuous Control with Posterior
  Sampling
Model-based Reinforcement Learning for Continuous Control with Posterior Sampling
Ying Fan
Yifei Ming
17
17
0
20 Nov 2020
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value
  Iteration
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration
Priyank Agrawal
Jinglin Chen
Nan Jiang
27
18
0
23 Oct 2020
Model-free conventions in multi-agent reinforcement learning with
  heterogeneous preferences
Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences
Raphael Köster
Kevin R. McKee
Richard Everett
Laura Weidinger
William S. Isaac
Edward Hughes
Edgar A. Duénez-Guzmán
T. Graepel
M. Botvinick
Joel Z Leibo
24
22
0
18 Oct 2020
Randomized Value Functions via Posterior State-Abstraction Sampling
Randomized Value Functions via Posterior State-Abstraction Sampling
Dilip Arumugam
Benjamin Van Roy
OffRL
28
7
0
05 Oct 2020
Novelty Search in Representational Space for Sample Efficient
  Exploration
Novelty Search in Representational Space for Sample Efficient Exploration
Ruo Yu Tao
Vincent François-Lavet
Joelle Pineau
30
43
0
28 Sep 2020
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of
  Multimodal Posteriors
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors
Yuling Yao
Aki Vehtari
Andrew Gelman
29
60
0
22 Jun 2020
Non-local Policy Optimization via Diversity-regularized Collaborative
  Exploration
Non-local Policy Optimization via Diversity-regularized Collaborative Exploration
Zhenghao Peng
Hao Sun
Bolei Zhou
13
18
0
14 Jun 2020
Reinforcement Learning with General Value Function Approximation:
  Provably Efficient Approach via Bounded Eluder Dimension
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension
Ruosong Wang
Ruslan Salakhutdinov
Lin F. Yang
23
55
0
21 May 2020
Adaptive Approximate Policy Iteration
Adaptive Approximate Policy Iteration
Botao Hao
N. Lazić
Yasin Abbasi-Yadkori
Pooria Joulani
Csaba Szepesvári
8
14
0
08 Feb 2020
Bayesian Residual Policy Optimization: Scalable Bayesian Reinforcement
  Learning with Clairvoyant Experts
Bayesian Residual Policy Optimization: Scalable Bayesian Reinforcement Learning with Clairvoyant Experts
Gilwoo Lee
Brian Hou
Sanjiban Choudhury
S. Srinivasa
BDL
OffRL
28
7
0
07 Feb 2020
Making Sense of Reinforcement Learning and Probabilistic Inference
Making Sense of Reinforcement Learning and Probabilistic Inference
Brendan O'Donoghue
Ian Osband
Catalin Ionescu
OffRL
14
47
0
03 Jan 2020
Implicit Generative Modeling for Efficient Exploration
Implicit Generative Modeling for Efficient Exploration
Neale Ratzlaff
Qinxun Bai
Fuxin Li
Wenyuan Xu
14
12
0
19 Nov 2019
Explicit Explore-Exploit Algorithms in Continuous State Spaces
Explicit Explore-Exploit Algorithms in Continuous State Spaces
Mikael Henaff
OffRL
14
31
0
01 Nov 2019
MAVEN: Multi-Agent Variational Exploration
MAVEN: Multi-Agent Variational Exploration
Anuj Mahajan
Tabish Rashid
Mikayel Samvelyan
Shimon Whiteson
DRL
137
355
0
16 Oct 2019
Behaviour Suite for Reinforcement Learning
Behaviour Suite for Reinforcement Learning
Ian Osband
Yotam Doron
Matteo Hessel
John Aslanides
Eren Sezener
...
Satinder Singh
Benjamin Van Roy
R. Sutton
David Silver
H. V. Hasselt
OffRL
21
178
0
09 Aug 2019
Global Optimality Guarantees For Policy Gradient Methods
Global Optimality Guarantees For Policy Gradient Methods
Jalaj Bhandari
Daniel Russo
35
185
0
05 Jun 2019
Never Forget: Balancing Exploration and Exploitation via Learning
  Optical Flow
Never Forget: Balancing Exploration and Exploitation via Learning Optical Flow
Hsuan-Kung Yang
Po-Han Chiang
Kuan-Wei Ho
Min-Fong Hong
Chun-Yi Lee
21
7
0
24 Jan 2019
A Short Survey on Probabilistic Reinforcement Learning
A Short Survey on Probabilistic Reinforcement Learning
R. Russel
11
2
0
21 Jan 2019
Optimization of Molecules via Deep Reinforcement Learning
Optimization of Molecules via Deep Reinforcement Learning
Zhenpeng Zhou
S. Kearnes
Li Li
R. Zare
Patrick F. Riley
AI4CE
16
532
0
19 Oct 2018
BaRC: Backward Reachability Curriculum for Robotic Reinforcement
  Learning
BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning
Boris Ivanovic
James Harrison
Apoorva Sharma
Mo Chen
Marco Pavone
OffRL
16
57
0
16 Jun 2018
Randomized Prior Functions for Deep Reinforcement Learning
Randomized Prior Functions for Deep Reinforcement Learning
Ian Osband
John Aslanides
Albin Cassirer
UQCV
BDL
21
373
0
08 Jun 2018
Exploration by Distributional Reinforcement Learning
Exploration by Distributional Reinforcement Learning
Yunhao Tang
Shipra Agrawal
OOD
36
30
0
04 May 2018
Gaussian-Dirichlet Posterior Dominance in Sequential Learning
Gaussian-Dirichlet Posterior Dominance in Sequential Learning
Ian Osband
Benjamin Van Roy
11
8
0
14 Feb 2017
Why is Posterior Sampling Better than Optimism for Reinforcement
  Learning?
Why is Posterior Sampling Better than Optimism for Reinforcement Learning?
Ian Osband
Benjamin Van Roy
BDL
6
255
0
01 Jul 2016
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