Modified Q-learning with distance metric and virtual target on path planning of mobile robot

•We propose an improved Q-learning for path planning of mobile robot.•The ideas of distance metric, moving target and modified Q function is integrated.•The effectiveness of the improved Q-learning is tested in twenty navigation maps.•The proposed improved Q-learning outperforms Q-learning.•The performance of improved Q-learning is comparable with other path planners. Path planning is an essential element in mobile robot navigation. One of the popular path planners is Q-learning – a type of reinforcement learning that learns with little or no prior knowledge of the environment. Despite the successful implementation of Q-learning reported in numerous studies, its slow convergence associated with the curse of dimensionality may limit the performance in practice. To solve this problem, an Improved Q-learning (IQL) with three modifications is introduced in this study. First, a distance metric is added to Q-learning to guide the agent moves towards the target. Second, the Q function of Q-learning is modified to overcome dead-ends more effectively. Lastly, the virtual target concept is introduced in Q-learning to bypass dead-ends. Experimental results across twenty types of navigation maps show that the proposed strategies accelerate the learning speed of IQL in comparison with the Q-learning. Besides, performance comparison with seven well-known path planners indicates its efficiency in terms of the path smoothness, time taken, shortest distance and total distance used.