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Parallel Implementation of Reinforcement Learning Q-Learning Technique for FPGA

Lucileide M. D. Da Silva, Matheus Torquato Orcid Logo, Marcelo A. C. Fernandes

IEEE Access, Volume: 7, Pages: 2782 - 2798

Swansea University Author: Matheus Torquato Orcid Logo

Abstract

Q-learning is an off-policy reinforcement learning technique, which has the main advantage of obtaining an optimal policy interacting with an unknown model environment. This paper proposes a parallel fixed-point Q-learning algorithm architecture implemented on field programmable gate arrays (FPGA) f...

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Published in: IEEE Access
ISSN: 2169-3536
Published: 2019
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa49022
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Abstract: Q-learning is an off-policy reinforcement learning technique, which has the main advantage of obtaining an optimal policy interacting with an unknown model environment. This paper proposes a parallel fixed-point Q-learning algorithm architecture implemented on field programmable gate arrays (FPGA) focusing on optimizing the system processing time. The convergence results are presented, and the processing time and occupied area were analyzed for different states and actions sizes scenarios and various fixed-point formats. The studies concerning the accuracy of the Q-learning technique response and resolution error associated with a decrease in the number of bits were also carried out for hardware implementation. The architecture implementation details were featured. The entire project was developed using the system generator platform (Xilinx), with a Virtex-6 xc6vcx240t-1ff1156 as the target FPGA.
College: College of Engineering
Start Page: 2782
End Page: 2798