Abstract
Learning by confirmation is a new learning approach, which combines two types of supervised learning strategies: reinforcement learning and learning by examples. In this paper, we show how this new strategy accelerates the learning process when some knowledge is introduced to the reinforcement algorithm. The learning proposal has been tested on a real-time device, a Lego Mindstorms NXT 2.0 robot that has been configured as an inverted pendulum. The methodology shows good performance and the results are quite promising.
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Acknowledgments
This work has been partially supported by the Spanish project DPI2009-14552-C02-01.
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Carpio, A., Santos, M., Martín, J.A. (2014). A First Approach of a New Learning Strategy: Learning by Confirmation. In: Sun, F., Li, T., Li, H. (eds) Knowledge Engineering and Management. Advances in Intelligent Systems and Computing, vol 214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37832-4_37
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DOI: https://doi.org/10.1007/978-3-642-37832-4_37
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