Publication
Exploiting domain symmetries in reinforcement learning with continuous state and action spaces
Conference Article
Conference
IEEE International Conference on Machine Learning and Applications (ICMLA)
Edition
8th
Pages
331-336
Doc link
http://dx.doi.org/10.1109/ICMLA.2009.41
File
Abstract
A central problem in Reinforcement Learning is how to deal with large state and action spaces. When the problem domain presents intrinsic symmetries, exploiting them can be key to achieve good performance. We analyze the gains that can be effectively achieved by exploiting different kinds of symmetries, and the effect of combining them, in a test case: the stand-up and stabilization of an inverted pendulum.
Categories
intelligent robots, learning (artificial intelligence).
Author keywords
domain symmetries, temporal symmetry, reinforcement learning, continuous state-action spaces
Scientific reference
A. Agostini and E. Celaya. Exploiting domain symmetries in reinforcement learning with continuous state and action spaces, 8th IEEE International Conference on Machine Learning and Applications, 2009, Miami, Florida, pp. 331-336.
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