| Summary: | 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. Experi�mental 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.
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