Summary
Q-learning in the Reinforcement Learning (RL) field is the powerful and attractive tool to make robots generate autonomous behavior. But it needs large amount of computational cost because of its discrete state and action. To generated smooth trajectory with less computational cost, we propose two ingredients for Q-learning. We applied Q-learning to the simulated two wheeled robot to generate trajectory for Ball-To-Goal task in robot soccer. …
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© 2006 Springer-Verlag Berlin Heidelberg
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Shimizu, M., Fujita, M., Miyamoto, H. (2006). Trajectory Generation for a Mobile Robot by Reinforcement Learning. In: Murase, K., Sekiyama, K., Naniwa, T., Kubota, N., Sitte, J. (eds) Proceedings of the 3rd International Symposium on Autonomous Minirobots for Research and Edutainment (AMiRE 2005). Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-29344-2_18
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DOI: https://doi.org/10.1007/3-540-29344-2_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28496-3
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