A gaussian process reinforcement learning algorithm with adaptability and minimal tuning requirements

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

2 Sitaatiot (Scopus)

Abstrakti

We present a novel Bayesian reinforcement learning algorithm that addresses model bias and exploration overhead issues. The algorithm combines different aspects of several state-of-the-art reinforcement learning methods that use Gaussian Processes model-based approaches to increase the use of the online data samples. The algorithm uses a smooth reward function requiring the reward value to be derived from the environment state. It works with continuous states and actions in a coherent way with a minimized need for expert knowledge in parameter tuning. We analyse and discuss the practical benefits of the selected approach in comparison to more traditional methodological choices, and illustrate the use of the algorithm in a motor control problem involving a two-link simulated arm.

AlkuperäiskieliEnglanti
OtsikkoArtificial Neural Networks and Machine Learning, ICANN 2014 - 24th International Conference on Artificial Neural Networks, Proceedings
KustantajaSpringer Verlag
Sivut371-378
Sivumäärä8
Vuosikerta8681 LNCS
ISBN (painettu)9783319111780
DOI - pysyväislinkit
TilaJulkaistu - 2014
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Artificial Neural Networks - Hamburg, Saksa
Kesto: 15 syysk. 201419 syysk. 2014
Konferenssinumero: 24

Julkaisusarja

NimiLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vuosikerta8681 LNCS
ISSN (painettu)03029743
ISSN (elektroninen)16113349

Conference

ConferenceInternational Conference on Artificial Neural Networks
LyhennettäICANN
Maa/AlueSaksa
KaupunkiHamburg
Ajanjakso15/09/201419/09/2014

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