Universitšt Karlsruhe
Fynesse: A new architecture for sequential decision problems

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.

Zusammenfassung

Reinforcement 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.

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Autoren

Alumni
Dr. Martin Spott
Partner
Dr. Martin Riedmiller
Ralf Schoknecht
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