NDLI: A scalable parallel Q-Learning algorithm for resource constrained decentralized computing environments

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A scalable parallel Q-Learning algorithm for resource constrained decentralized computing environments

Content Provider	ACM Digital Library
Author	Latré, Steven Famaey, Jeroen Camelo, Miguel
Abstract	The Internet of Things (IoT) is more and more becoming a platform for mission critical applications with stringent requirements in terms of response time and mobility. Therefore, a centralized High Performance Computing (HPC) environment is often not suitable or simply non-existing. Instead, there is a need for a scalable HPC model that supports the deployment of applications on the decentralized but resource constrained devices of the IoT. Recently, Reinforcement Learning (RL) algorithms have been used for decision making within applications by directly interacting with the environment. However, most RL algorithms are designed for centralized environments and are time and resource consuming. Therefore, they are not applicable to such constrained decentralized computing environments. In this paper, we propose a scalable Parallel Q-Learning (PQL) algorithm for resource constrained environments. By combining a table partition strategy together with a co-allocation of both processing and storage, we can significantly reduce the individual resource cost and, at the same time, guarantee convergence and minimize the communication cost. Experimental results show that our algorithm reduces the required training in proportion of the number of Q-Learning agents and, in terms of execution time, it is up to 24 times faster than several well-known PQL algorithms.
Starting Page	27
Ending Page	35
Page Count	9
File Format	PDF
ISBN	9781509038824
DOI	10.1109/MLHPC.2016.7
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-11-13
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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