Acknowledgement
This work was supported by the research grant of the KODISA Scholarship Foundation in 2023.
References
- Angel Martinez-Tenor, Juan-Antonio Fernandez-Madrigal, Ana Cruz-Martin. LEGO Mindstorms nxt and q-learning: a teaching approach for robotics in engineering. 7th international Conference of Education, Research and Innovation (ICERI) 2014.
- Ke X., Wu, F., Zhao, J. (2015). Simplified Online Q-Learning for LEGO EV3 Robot. IEEE International Conference on Control System, Computing and Engineering, 27-29 November 2015, Penang Malaysia. https://doi.org/https://doi.org/10.1109/ICCSCE.2015.7482161
- Kim, B. C., Kim, S.K., Yoon B. J. (2002). Online Reinforcement Learning to Search the Shortest Path in Maze Environments. Journal of Korea Information Processing Society, 9-B, 155-162. https://doi.org/10.3745/KIPSTB.2002.9B.2.155
- LEGO Mindstorms. http://mindstorms.lego.com/. Retrieved Jul. 2015
- LEGO, Mindstorms. https://www.lego.com/en-gb/themes/mindstorms/. (2022)
- Oh, I. S. (2017), Machine Learning. Published by Hanbit Academy, Inc. Printed in Korea. ISBN : 979-11-5664-158-2
- Park, S., Lee, S., Kim, H. (2021), Study on Sensing-based Robot Control System in Constrained Environment, Spring Conference Korean Institute of Next Generation Computing. 102-103, 2021
- Watkins C.J.C.H. (1992), Q-Learning. Technical Note. Machine Learning, 8, 279-292. Kluwer Academic Publishers, Boston https://doi.org/10.1023/A:1022676722315