Abstract
This paper presents a comparative study of three parallel implementation models for reinforcement learning. Two of them utilize Q-learning, and the other one utilizes fuzzy Q-learning for agent learning. In order to evaluate performance and validity of the three method, a PC (personal computer) cluster system consisting of 16 PCs connected via Gigabit ethernet has been built. For communications to deliver data among PCs, MPI (Message Passing Interface) is employed. Experimental results are compared with one another to show the performance and characteristics of the three methods.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.