An Experimental Study for Tracking Ability of Deep Q-Network under the Multi-Objective Behaviour using a Mobile Robot with LiDAR
Masashi Sugimoto, Ryunosuke UCHIDA, 伸二 都築, Hitoshi SORI, Hiroyuki INOUE, Kentarou Kurashige, Shiro Urushihara · 2021 International Symposium on Electrical, Electronics and Information Engineering · 2021
The Reinforcement Learning (RL) had been attracting attention for a long time that because it can be easily applied to real robots. On the other hand, in Q-Learning one of RL methods, since it contains the Q-table and grind environment is updated, especially, a large amount of Q-tables are required to express continuous “states,” such as smooth movements of the robot arm. Moreover, there was a disadvantage that calculation could not be performed real-time in case of amount of states and actions.