A Deterministic Improved Q-Learning for Path Planning of a Mobile Robot

Konar, A. and Chakraborty, I.G. and Singh, S.J. and Jain, L.C. and Nagar, Atulya K. (2013) A Deterministic Improved Q-Learning for Path Planning of a Mobile Robot. Systems, Man, and Cybernetics: Systems, IEEE Transactions on, 43 (5). pp. 1141-1153. ISSN 2168-2216

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Abstract

This paper provides a new deterministic Q-learning with a presumed knowledge about the distance from the current state to both the next state and the goal. This knowledge is efficiently used to update the entries in the Q-table once only by utilizing four derived properties of the Q-learning, instead of repeatedly updating them like the classical Q-learning. Naturally, the proposed algorithm has an insignificantly small time complexity in comparison to its classical counterpart. Furthermore, the proposed algorithm stores the Q-value for the best possible action at a state and thus saves significant storage. Experiments undertaken on simulated maze and real platforms confirm that the Q-table obtained by the proposed Q-learning when used for the path-planning application of mobile robots outperforms both the classical and the extended Q-learning with respect to three metrics: traversal time, number of states traversed, and 90° turns required. The reduction in 90° turnings minimizes the energy consumption and thus has importance in the robotics literature.

Item Type:	Article
Additional Information and Comments:	A Deterministic Improved Q-Learning for Path Planning of a Mobile Robot Konar, A.; Chakraborty, I.G. ; Singh, S.J. ; Jain, L.C. ; Nagar, A.K. Systems, Man, and Cybernetics: Systems, IEEE Transactions, 43 (5), p.1141-1153 10.1109/TSMCA.2012.2227719
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department:	Faculty of Human and Digital Sciences > Mathematics and Computer Science
Depositing User:	Users 3 not found.
Date Deposited:	05 Mar 2014 15:35
Last Modified:	21 Feb 2016 03:26
URI:	https://hira.hope.ac.uk/id/eprint/231

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