چکیده مقاله
This paper discusses solving the Grid World with Changing Obstacles GWCO problem with the Deep Reinforcement Learning Deep RL method In the GWCO problem, obstacles move on specific paths Moving these obstacles turns this problem into a dynamic problem Due to the changing environment of the problem and the high number of state action, the Deep RL method is used to solve the GWCO problem In this paper, we refer to the methods of Reinforcement Learning, Deep Learning, and Deep RL, and some of their applications In the final section, by comparing the three types of learning rate α , the simulation results are compared and it can be concluded that for the GWCO problem, the learning rate is better set to 0 001 Simulation of this paper is done with the powerful Python software and Tensorflow
کلیدواژهها
نویسندگان
محمد حسن علیائی طرقبه
Faculty of Electrical Engineering, Sadjad University of Technology, Mashhad, Iran
Hasan Jalali
Faculty of Electrical Engineering, Sadjad University of Technology, Mashhad, Iran
علی علیائی طرقبه
Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Amin Noori
Faculty of Electrical Engineering, Sadjad University of Technology, Mashhad, Iran
شیوه ارجاع
Olyaei Torqabeh, Mohammad Hasan and Jalali, Hasan and Olyaei Torqabeh, Ali and Noori, Amin,1396,Deep Reinforcement Learning for Grid world with changing obstacles and investigating the effect of learning rate on received rewards,Fifth International Conference on Electrical and Computer Engineering with Emphasis on Indigenous Knowledge,Tehran
ارائهشده در
مجموعه مقالات پنجمین کنفرانس بین المللی مهندسی برق و کامپیوتر با تاکید بر دانش بومی19 بهمن 1396 · تهران