Hierarchical Relative Entropy Policy Search An Information Theoretic Learning Algorithm in Multimodal Solution Spaces for Real Robots
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- Englisch ausgewählt
Fr. 46.90
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
04.01.2014
Verlag
AV AkademikerverlagSeitenzahl
68
Maße (L/B/H)
22/15/0.5 cm
Gewicht
119 g
Sprache
Englisch
ISBN
978-3-639-47599-9
Many real-world problems are inherently hierarchically structured. The use of this structure in an agent's policy may well be the key to improved scalability and higher performance on motor skill tasks. However, such hierarchical structures cannot be exploited by current policy search algorithms. We concentrate on a basic, but highly relevant hierarchy - the `mixed option' policy. Here, a gating network first decides which of the options to execute and, subsequently, the option-policy determines the action. Using a hierarchical setup for our learning method allows us to learn not only one solution to a problem but many. We base our algorithm on a recently proposed information theoretic policy search method, which addresses the exploitation-exploration trade-off by limiting the loss of information between policy updates.
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