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http://dx.doi.org/10.7471/ikeee.2020.24.4.991

Real-Time Path Planning for Mobile Robots Using Q-Learning  

Kim, Ho-Won (Dept. of Smart Robot Convergence and Application Engineering, Pukyong National University)
Lee, Won-Chang (Dept. of Electronic Engineering, Pukyong National University)
Publication Information
Journal of IKEEE / v.24, no.4, 2020 , pp. 991-997 More about this Journal
Abstract
Reinforcement learning has been applied mainly in sequential decision-making problems. Especially in recent years, reinforcement learning combined with neural networks has brought successful results in previously unsolved fields. However, reinforcement learning using deep neural networks has the disadvantage that it is too complex for immediate use in the field. In this paper, we implemented path planning algorithm for mobile robots using Q-learning, one of the easy-to-learn reinforcement learning algorithms. We used real-time Q-learning to update the Q-table in real-time since the Q-learning method of generating Q-tables in advance has obvious limitations. By adjusting the exploration strategy, we were able to obtain the learning speed required for real-time Q-learning. Finally, we compared the performance of real-time Q-learning and DQN.
Keywords
reinforcement learning; Q-learning; DQN; path planning; mobile robot;
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