ITM Web Conf.
Volume 47, 20222022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
|Number of page(s)
|Algorithm Optimization and Application
|23 June 2022
Improved reinforcement learning algorithm for mobile robot path planning
School of Information and Control Engineering, Liaoning Petrochemical University, Fushun, China
* Corresponding author: firstname.lastname@example.org
In order to solve the problem that traditional Q-learning algorithm has a large number of invalid iterations in the early convergence stage of robot path planning, an improved reinforcement learning algorithm is proposed. Firstly, the gravitational potential field in the improved artificial potential field algorithm is introduced when the Q table is initialized to accelerate the convergence. Secondly, the Tent Chaotic Mapping algorithm is added to the initial state determination process of the algorithm, which allows the algorithm to explore the environment more fully. In addition, an ε-greed strategy with the number of iterations changing the ε value becomes the action selection strategy of the algorithm, which improves the performance of the algorithm. Finally, the grid map simulation results based on MATLAB show that the improved Q-learning algorithm has greatly reduced the path planning time and the number of non-convergence iterations compared with the traditional algorithm.
Key words: Reinforcement learning / Greedy strategy / Mobile robot / Path planning
© The Authors, published by EDP Sciences, 2022
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