AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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Rezumat articol ediţie STUDIA UNIVERSITATIS BABEŞ-BOLYAI În partea de jos este prezentat rezumatul articolului selectat. Pentru revenire la cuprinsul ediţiei din care face parte acest articol, se accesează linkul din titlu. Pentru vizualizarea tuturor articolelor din arhivă la care este autor/coautor unul din autorii de mai jos, se accesează linkul din numele autorului. |
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STUDIA INFORMATICA - Ediţia nr.Sp.Issue 1 din 2009 | |||||||
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Q-LEARNING AND POLICY GRADIENT METHODS. Autori: HUNOR JAKAB, LEHEL CSATÓ. |
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Rezumat: Many real-world tasks require a robotic agent to adapt its behavior to certain environmental conditions and to acquire knowledge without user interaction. In reinforcement learning knowledge is usually acquired without preexisting training data, there by making the learning process more "natural". In this paper we investigate two reinforcement learning methods and present a simulation environment where we test their performance. The simulation environment allows the testing of various reinforcement algorithms without a need for the physical robot. Its advantage is that it can be used to perform benchmarks and evaluations of different learning algorithms. Key words and phrases. reinforcement learning, Markov decision processes, robotics. |
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