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Reinforcement Learning for Robots Using Neural Networks
- Source :
- DTIC AND NTIS
- Publication Year :
- 1993
-
Abstract
- Reinforcement learning agents are adaptive, reactive, and self-supervised. The aim of this dissertation is to extend the state of the art of reinforcement learning and enable its applications to complex robot-learning problems. In particular, it focuses on two issues. First, learning from sparse and delayed reinforcement signals is hard and in general a slow process. Techniques for reducing learning time must be devised. Second, most existing reinforcement learning methods assume that the world is a Markov decision process. This assumption is too strong for many robot tasks of interest, This dissertation demonstrates how one can possibly overcome the slow learning problem and tackle non-Markovian environments, making reinforcement learning more practical for realistic robot tasks.
Details
- Database :
- OAIster
- Journal :
- DTIC AND NTIS
- Notes :
- text/html, English
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.ocn831990390
- Document Type :
- Electronic Resource