Issues in putting reinforcement learning onto robots:

Abstract: "There has recently been a good deal of interest in robot learning. Reinforcement Learning (RL) is a trial and error approach to learning that has recently become popular with roboticists. This is despite the fact that RL methods are very slow, and scale badly with the size of the sta...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
1. Verfasser: Wyatt, Jeremy (VerfasserIn)
Format: Buch
Sprache:English
Veröffentlicht: Edinburgh 1996
Schriftenreihe:University <Edinburgh> / Department of Artificial Intelligence: DAI research paper 784
Schlagworte:
Zusammenfassung:Abstract: "There has recently been a good deal of interest in robot learning. Reinforcement Learning (RL) is a trial and error approach to learning that has recently become popular with roboticists. This is despite the fact that RL methods are very slow, and scale badly with the size of the state and action spaces, thus making them difficult to put onto real robots. This paper describes some work I have been doing on trying to understand why RL methods are so slow and on how they might be speeded up. A reinforcement learning algorithm loosely based on the theory of hypothesis testing is presented as are some preliminary results from employing this algorithm on a set of bandit problems."
Beschreibung:8 S.

Es ist kein Print-Exemplar vorhanden.

Fernleihe Bestellen Achtung: Nicht im THWS-Bestand!