Abstract
This paper presents a method for behavior learning of an autonomous agent using modified Learning Vector Quantization (LVQ) with fuzzy sets in continuous state space. When the environment is a continuous state space, it has infinitely many state values. So, it is impossible to learn a good action to take in each of the state values. This paper uses a function approximation technique based on the LVQ algorithm to learn actions of agent in continuous state space. An advantage of this technique is that it can represent the mapping between the continuous-valued state space and appropriate actions with a finite number of parameters. An example illustrates its validity in continuous space problems.
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© 2004 Springer-Verlag Berlin Heidelberg
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Shon, MK., Murata, J. (2004). Behavior Learning of Autonomous Agents in Continuous State Using Function Approximation. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30132-5_164
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DOI: https://doi.org/10.1007/978-3-540-30132-5_164
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23318-3
Online ISBN: 978-3-540-30132-5
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