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| Publications [#243797] of Mauro Maggioni
Papers Published
- Mahadevan, S; Maggioni, M; Ferguson, K; Osentoski, S, Learning representation and control in continuous Markov decision processes,
Proceedings of the National Conference on Artificial Intelligence, vol. 2
(November, 2006),
pp. 1194-1199
(last updated on 2019/04/10)
Abstract:
This paper presents a novel framework for simultaneously learning representation and control in continuous Markov decision processes. Our approach builds on the framework of proto-value functions, in which the underlying representation or basis functions are automatically derived from a spectral analysis of the state space manifold. The proto-value functions correspond to the eigenfunctions of the graph Laplacian. We describe an approach to extend the eigenfunctions to novel states using the Nyström extension. A least-squares policy iteration method is used to learn the control policy, where the underlying subspace for approximating the value function is spanned by the learned proto-value functions. A detailed set of experiments is presented using classic benchmark tasks, including the inverted pendulum and the mountain car, showing the sensitivity in performance to various parameters, and including comparisons with a parametric radial basis function method. Copyright © 2006, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.
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