Abstract
A well known model of reinforcement learning is called Adaptive Heuristic Critic learning. It is composed of two so called “neuronlike adaptive elements” and has been used to solve difficult learning control problems. In this paper we present an FPGA design and implementation of such algorithm, and, furthermore, we describe a neurocontroller system composed of a network of neuronlike adaptive elements and an unsupervised clustering system called FAST, which dynamically partitions the input state space of the system being controlled.
A. Pérez-Uribe is supported by the Centre Suisse d'électronique et de Microtechnique CSEM, Neuchâtel, Switzerland.
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Pérez-Uribe, A., Sanchez, E. (1997). FPGA implementation of a network of neuronlike adaptive elements. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020322
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DOI: https://doi.org/10.1007/BFb0020322
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