Solving the optimal path planning of a mobile robot using improved Q-learning.

Abstract Q-learning, a type of reinforcement learning, has gained increasing popularity in autonomous mobile robot path planning recently, due to its self-learning ability without requiring a priori model of the environment. Yet, despite such advantage, Q-learning exhibits slow convergence to the optimal solution. In order to address this limitation, the concept of partially guided Q-learning is introduced wherein, the flower pollination algorithm (FPA) is utilized to improve the initialization of Q-learning. Experimental evaluation of the proposed improved Q-learning under the challenging environment with a different layout of obstacles shows that the convergence of Q-learning can be accelerated when Q-values are initialized appropriately using the FPA. Additionally, the effectiveness of the proposed algorithm is validated in a real-world experiment using a three-wheeled mobile robot. Highlights • We propose an improved Q-learning to solve path planning of a mobile robot. • The flower pollination algorithm is used to initialize the Q-table prior to the implementation of Q-learning. • Its effectiveness is tested in solving the optimal path in different test cases. • Performance comparison with classical Q-learning and other modified Q-learning is made. • The proposed model shows improvement in terms of computational time than others. [ABSTRACT FROM AUTHOR]