Abstract
Reinforcement learning (RL) for a linear family of tasks is studied in this paper. The key of our discussion is nonlinearity of the optimal solution even if the task family is linear; we cannot obtain the optimal policy by a naive approach. Though there exists an algorithm for calculating the equivalent result to Q-learning for each task all together, it has a problem with explosion of set sizes. We introduce adaptive margins to overcome this difficulty.
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Hiraoka, K., Yoshida, M., Mishima, T. (2008). Parallel Reinforcement Learning for Weighted Multi-criteria Model with Adaptive Margin. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69158-7_51
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DOI: https://doi.org/10.1007/978-3-540-69158-7_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69154-9
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