Authors: Tsubasa Asano; Satoshi Yamada
Addresses: Department of Intelligent Mechanical Engineering, Okayama University of Science, 1-1 Ridai-cho, Kita-ku, Okayama 700-0005, Japan. ' Department of Intelligent Mechanical Engineering, Okayama University of Science, 1-1 Ridai-cho, Kita-ku, Okayama 700-0005, Japan
Abstract: A modular reinforcement learning system (modular RL) with adaptive networks was proposed for applying the reinforcement learning into control tasks with numerous inputs. It is composed of several control modules and a selection module. All its modules are calculated by using the incremental normalised Gaussian networks (INGnet). The modular RL (INGnet) showed a better learning ability in all three control tasks performed in this study than the modular reinforcement learning whose all modules are calculated by CMAC [modular RL (CMAC)]. It showed a better or similar learning ability to the reinforcement learning using INGnet [RL (INGnet)]. From the simulation results obtained in this study, the modular RL (INGnet) is considered to have a better learning ability in the control tasks with a large number of inputs (8-10) than the modular RL (CMAC) and the RL (INGnet).
Keywords: autonomous robots; learning control; modular reinforcement learning; incremental normalised Gaussian network; adaptive networks; simulation; robot control.
International Journal of Advanced Mechatronic Systems, 2012 Vol.4 No.2, pp.94 - 102
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 03 Aug 2012 *