Modular reinforcement learning for control problems with multi sensors
by Hayato Nakama; Satoshi Yamada
International Journal of Advanced Mechatronic Systems (IJAMECHS), Vol. 3, No. 4, 2011

Abstract: The modular reinforcement learning system, which is composed of some control modules and a selection module, was developed to apply to the task where several types of sensor information were necessary for the control. In this study, the modular reinforcement learning was applied to the task where the second order correlation of two different types of sensors must be discriminated. The target (goal) is a bar with the correct mark with a lamp, and other objects have one of them or another mark. To discriminate between the target and other objects, the 'AND' condition of the light sensor and camera information must be distinguished. Since the learning efficiency was low, the iterative learning and the initial learning were proposed. As a result, the appropriate module selections and action selections were trained by the modular reinforcement learning.

Online publication date: Wed, 18-Mar-2015

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Mechatronic Systems (IJAMECHS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com