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Abstract: The central thesis of this article is that memory-based methods provide natural and powerful mechanisms for high-autonomy learning control. This paper takes the form of a survey of the ways in which memory-based methods can and have been applied to control tasks, with an emphasis on tasks in robotics and manufacturing. We explain the various forms that control tasks can take, and how this impacts on the choice of learning algorithm. We show a progression of five increasingly more complex... (Update)
Context of citations to this paper: More
...environment. Several methods have been proposed in this direction : Learning by demonstrations or from examples, Memory based learning [19, 1, 13] , Imitation [12, 11, 2, 14, 7] or Supervised Learning [21, 20] Those approaches focus on the learning of complex action sequences...
...experience to the wrong states. In the learning methods above, we can distinguish between representational tools and learning paradigms [23]. The representational tools in VRDP and parti game are kd trees. They are used to partition the state space, approximate the value...
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BibTeX entry: (Update)
Moore, A.W., Atkeson, C.G, and Schaal, S. (1995). Memory-based learning for control. Technical Report CMU-RI-TR-95-18, CMU Robotics Institute, 1995. http://citeseer.ist.psu.edu/article/moore95memorybased.html More
@misc{ moore95memorybased,
author = "A. Moore and C. Atkeson and S. Schaal",
title = "Memory-based learning for control",
text = "Moore, A.W., Atkeson, C.G, and Schaal, S. (1995). Memory-based learning
for control. Technical Report CMU-RI-TR-95-18, CMU Robotics Institute, 1995.",
year = "1995",
url = "citeseer.ist.psu.edu/article/moore95memorybased.html" }
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