Value Iteration on Multicore Processors

被引：0

作者：

Jain, Anuj ^{[1
]}

Sahni, Sartaj ^{[2
]}

机构：

[1] Adobe Syst Inc, Lehi, UT 84043 USA

[2] Univ Florida, CISE Dept, Gainesville, FL USA

来源：

2020 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT 2020) | 2020年

基金：

美国国家科学基金会;

关键词：

Reinforcement Learning; Markov Decision Process; Value Iteration; Multicore; Parallelization;

D O I：

10.1109/ISSPIT51521.2020.9408773

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Value Iteration (VI) is a powerful, though time consuming, approach to solve reinforcement learning problems modeled as Markov Decision Processes (MDPs). In this paper, we explore strategies to run the sate-of-the-art cache efficient algorithm for VI developed by us [1], [2] on a multicore processor. We demonstrate a speedup of up to 2.59 on a 10-core multiprocessor using 20 threads on popular benchmark data. The speedup for the parallelized portion of the computation is up to 5.89.

引用

页数：7

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