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Scalable Bayesian Reinforcement Lear...

Lee, Gilwoo.

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Scalable Bayesian Reinforcement Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Scalable Bayesian Reinforcement Learning./
Author:	Lee, Gilwoo.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	113 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-05, Section: B.
Contained By:	Dissertations Abstracts International82-05B.
Subject:	Artificial intelligence. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28091288
ISBN:	9798684669699

Scalable Bayesian Reinforcement Learning.
Lee, Gilwoo.

Scalable Bayesian Reinforcement Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 113 p.

Source: Dissertations Abstracts International, Volume: 82-05, Section: B.

Thesis (Ph.D.)--University of Washington, 2020.

This item must not be sold to any third party vendors.

Informed and robust decision making in the face of uncertainty is critical for robots operating in unstructured environments. We formulate this problem as Bayesian Reinforcement Learning (BRL) over latent Markov Decision Processes (MDPs). While Bayes-optimality is theoretically the gold standard, existing algorithms scale poorly to continuous state and action spaces. This thesis proposes a set of BRL algorithms that scale to complex control tasks. Our algorithms build on the following insight: robotics problems have structural priors that we can use to produce approximate models and experts that the agent can leverage.First, we propose an algorithm which improves a nominal model and policy with data-driven semi-parametric learning and optimal control. Then, we look into more general BRL tasks with complex latent models. We propose algorithms that combine batch reinforcement learning algorithms with experts to scale to complex latent tasks. Finally, through simulated and physical experiments, we demonstrate that our algorithms drastically outperform existing adaptive RL methods.

ISBN: 9798684669699Subjects--Topical Terms:

516317
Artificial intelligence.
Subjects--Index Terms:

Adaptive Control

Scalable Bayesian Reinforcement Learning.
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260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 113 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-05, Section: B.
500 $a Advisor: Srinivasa, Siddhartha S.
502 $a Thesis (Ph.D.)--University of Washington, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Informed and robust decision making in the face of uncertainty is critical for robots operating in unstructured environments. We formulate this problem as Bayesian Reinforcement Learning (BRL) over latent Markov Decision Processes (MDPs). While Bayes-optimality is theoretically the gold standard, existing algorithms scale poorly to continuous state and action spaces. This thesis proposes a set of BRL algorithms that scale to complex control tasks. Our algorithms build on the following insight: robotics problems have structural priors that we can use to produce approximate models and experts that the agent can leverage.First, we propose an algorithm which improves a nominal model and policy with data-driven semi-parametric learning and optimal control. Then, we look into more general BRL tasks with complex latent models. We propose algorithms that combine batch reinforcement learning algorithms with experts to scale to complex latent tasks. Finally, through simulated and physical experiments, we demonstrate that our algorithms drastically outperform existing adaptive RL methods.
590 $a School code: 0250.
650 4 $a Artificial intelligence. $3 516317
650 4 $a Robotics. $3 519753
650 4 $a Statistical physics. $3 536281
650 4 $a Applied mathematics. $3 2122814
653 $a Adaptive Control
653 $a Bayesian Reinforcement Learning
653 $a Robust Reinforcement Learning
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