Maze Search Using Reinforcement Learning by a Mobile Robot
Journal Title: Advances in Robotics & Mechanical Engineering - Year 2018, Vol 1, Issue 2
Abstract
This review presents on research of application of reinforcement learning and new approaches on a course search in mazes with some kinds of multi-point passing as machines. It is based on a selective learning from multi-directive behavior patterns using PS (Profit Sharing) by an agent. The behavior is selected stochastically from 4 kinds of ones using PS with Boltzmann Distribution with a plan to inhibit invalid rules by a reinforcement function of a geometric sequence. Moreover, a variable temperature scheme is adopted in this distribution, where the environmental identification is valued in the first stage of the search and the convergence of learning is shifted to be valuing as time passing. A SUB learning system and a multistage layer system were proposed in this review, and these functions were inspected by some simulations and experiments using a mobile robot.In robots which has begun to spread to not only industrial world but also general home, e.g. cleaning robots etc., recently achievement of complex tasks and adaptation of complex environment has been required and can be done by agents which were concept of distributed artificial intelligent and caught abstractly various robots. Conventionally, as behavior of agents has been controlled by rules designed as if then rules, a lot of rules were required for adaptation to complex environment and achievement of complex tasks.
Authors and Affiliations
Makoto Katoh, Keiichi Tanaka, Shunsuke Shikichi
Keywords
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- EP ID EP580565
- DOI 10.32474/ARME.2018.01.000110
- Views 106
- Downloads 0
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