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Double-Oracle Deep Reinforcement Learning for Handling Exponential Action Space in Sequential Stackelberg Security Games.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Double-Oracle Deep Reinforcement Learning for Handling Exponential Action Space in Sequential Stackelberg Security Games.
作者:	Tan, Czander.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2022
面頁冊數:	41 p.
附註:	Source: Masters Abstracts International, Volume: 84-05.
附註:	Advisor: Nguyen, Thanh H.
Contained By:	Masters Abstracts International84-05.
標題:	Computer science.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29252657
ISBN:	9798352946459

Double-Oracle Deep Reinforcement Learning for Handling Exponential Action Space in Sequential Stackelberg Security Games.
Tan, Czander.

Double-Oracle Deep Reinforcement Learning for Handling Exponential Action Space in Sequential Stackelberg Security Games. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 41 p.

Source: Masters Abstracts International, Volume: 84-05.

Thesis (M.S.)--University of Oregon, 2022.

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

Standard Stackelberg Security Games (SSGs) assume attackers to be myopic players that select only a single target based on the defender's strategy. In this paper, we consider sequential SSGs, in which attackers launch multiple attacks sequentially. With a sequence of events, however, the defender's action space grows exponentially with the number of time steps, making the problem computationally intractable.To handle this issue, this paper presents the following contributions. First, we use the Double Oracle algorithm to iteratively derive player strategies. Second, we use Advantage Actor-Critic models to approximate best response policies for both players. Lastly, we represent the defender action space compactly with marginal probabilities instead of enumerating all possible actions.Overall, our experiments show that the Double Oracle algorithm not only allows us to search through defender strategies effectively and efficiently, but also provides optimal solutions that outperform other models at scalable settings.

ISBN: 9798352946459Subjects--Topical Terms:

199325
Computer science.
Subjects--Index Terms:

Double-oracle deep reinforcement learning

Double-Oracle Deep Reinforcement Learning for Handling Exponential Action Space in Sequential Stackelberg Security Games.
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500 $a Source: Masters Abstracts International, Volume: 84-05.
500 $a Advisor: Nguyen, Thanh H.
502 $a Thesis (M.S.)--University of Oregon, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Standard Stackelberg Security Games (SSGs) assume attackers to be myopic players that select only a single target based on the defender's strategy. In this paper, we consider sequential SSGs, in which attackers launch multiple attacks sequentially. With a sequence of events, however, the defender's action space grows exponentially with the number of time steps, making the problem computationally intractable.To handle this issue, this paper presents the following contributions. First, we use the Double Oracle algorithm to iteratively derive player strategies. Second, we use Advantage Actor-Critic models to approximate best response policies for both players. Lastly, we represent the defender action space compactly with marginal probabilities instead of enumerating all possible actions.Overall, our experiments show that the Double Oracle algorithm not only allows us to search through defender strategies effectively and efficiently, but also provides optimal solutions that outperform other models at scalable settings.
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