| Konferenzartikel| [zwingend] | Ralf Schoknecht, Martin Spott, Martin Riedmiller, Fynesse: A new architecture for sequential decision problems, Computational Intelligence im industriellen Einsatz, VDI-Berichte, p. 109-118, May 2000.
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ZusammenfassungReinforcement learning is an optimization technique for applications
like control or scheduling problems. It is used in learning
situations, where success and failure of the system are the only
training information. Unfortunately, we have to pay a price for this
powerful ability: long training times and the instability of the
learning process are not tolerable for industrial applications
with large continuous state spaces. From our point of view, the
integration of prior knowledge is a key mechanism for making
autonomous learning practicable for industrial applications. The
learning control architecture Fynesse provides a unified view
onto the integration of prior control knowledge in the
reinforcement learning framework. In this way, other approaches in
this area can be interpreted as special cases of Fynesse. The key
features of Fynesse are (1) the integration of prior control knowledge
like linear controllers, control characteristics or fuzzy
controllers, (2) autonomous learning of control strategies and (3)
the interpretation of learned strategies in terms of fuzzy control
rules. The benefits and problems of different methods for the
integration of a priori knowledge are demonstrated on empirical
studies.
Autoren
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