The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON SYSTEMS
Transactions ID Number: 31-514
Full Name: Hossam Ali
Position: Ph.D. Candidate
Age: ON
Sex: Male
Address: Faculty of Engineering, Helwan university, 1 Sherief st. Helwan, Egypt
Country: EGYPT
Tel:
Tel prefix:
Fax:
E-mail address: hosam_ib_ali@yahoo.com
Other E-mails:
Title of the Paper: Compact Q-learning Optimized for Complicated Behaviors and Robots having Small memory and Processing Power
Authors as they appear in the Paper: Elsayed Saad, Medhat Awadalla, Alaa Hamdy, Hossam Ali
Email addresses of all the authors: elsayedmos@hotmail.com awadalla_medhat@yahoo.co.uk, alaa.hamdy@gmail.com, Hosam_ib_ali@yahoo.com
Number of paper pages: 10
Abstract: Abstract: - This paper focuses on developing a team of mobile robots capable of learning via human interaction. A modified Q-learning algorithm incorporating a teacher is proposed. The paper first concentrates on simplifying the Q-learning algorithm to be implemented on small and simple team of robots having limited capabilities of memory and computational power. Second it concentrates on the incorporation of a human teacher in the Q-learning algorithm. Real and simulated experiments using the well-known robot simulator Webots on a proof of context both single and multi-target tracking tasks have been conducted. The achieved results show the success of the proposed algorithm in the overall system performance.
Keywords: Machine learning; Human-robot interaction; Reinforcement learning; Q-learning
EXTENSION of the file: .pdf
Special (Invited) Session:
Organizer of the Session:
How Did you learn about congress:
IP ADDRESS: 41.232.58.225
