Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—Q Learning is a form of reinforcement learning for path finding problems that does not require a model of the environment. It allows the agent to explore the given environment and the learning is achieved by maximizing the rewards for the set of actions it takes. In the recent times, Q Learning approaches have proven to be successful in various applications ranging from navigation systems to video games. This paper proposes a Q learning based method that supports path planning for robots. The paper also discusses the choice of parameter values and suggests optimized parameters when using such a method. The performance of the most popular path finding algorithms such as A* and Dijkstra algorithm have been compared to the Q learning approach and were able to outperform Q learning with respect to computation time and resulting path length.
Index Terms—reinforcement learning, Q learning, robot navigation, path planning, path finding, shortest path
Cite: Phalgun Chintala, Rolf Dornberger, and Thomas Hanne, "Robotic Path Planning by Q Learning and a Performance Comparison with Classical Path Finding Algorithms," International Journal of Mechanical Engineering and Robotics Research, Vol. 11, No. 6, pp. 373-378, June 2022. DOI: 10.18178/ijmerr.11.6.373-378
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