Abstract
We present an algorithmic approach for integrated learning and planning in predictive representations. The approach extends earlier work on predictive state representations to the case of online exploration, by allowing exploration of the domain to proceed in a goal-directed fashion and thus be more efficient. Our algorithm interleaves online learning of the models, with estimation of the value function. The framework is applicable to a variety of important learning problems, including scenarios such as apprenticeship learning, model customization, and decision-making in non-stationary domains.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aberdeen, D., Buffet, O., Thomas, O.: Policy-gradients for PSRs and POMDPs. In: AISTATS (2007)
Boots, B., Gordon, G.J.: An online spectral learning algorithm for partially observable nonlinear dynamical systems. In: Proceedings AAAI (2011)
Boots, B., Siddiqi, S., Gordon, G.: Closing the learning-planning loop with predictive state representations. In: Proceedings of Robotics: Science and Systems (2010)
Bowling, M., McCracken, P., James, M., Neufeld, J., Wilkinson, D.: Learning predictive state representations using non-blind policies. In: Proceedings ICML (2006)
Brand, M.: Fast low-rank modifications of the thin singular value decomposition. Linear Algebra and its Applications 415, 20–30 (2006)
Dinculescu, M., Precup, D.: Approximate predictive representations of partially observable systems. In: Proceedings ICML (2010)
Ernst, D., Geurts, P., Wehenkel, L.: Tree-based batch mode reinforcement learning. Journal of Machine Learning (2005)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Machine Learning 63, 3–42 (2006)
Gordon, G.J.: Approximate Solutions to Markov Decision Processes. Ph.D. thesis, School of Computer Science, Carnegie Mellon University (1999)
Izadi, M.T., Precup, D.: Point-Based Planning for Predictive State Representations. In: Bergler, S. (ed.) Canadian AI. LNCS (LNAI), vol. 5032, pp. 126–137. Springer, Heidelberg (2008)
James, M.R., Wessling, T., Vlassis, N.: Improving approximate value iteration using memories and predictive state representations. In: AAAI (2006)
James, M.R., Singh, S., Littman, M.L.: Planning with predictive state representations. In: International Conference on Machine Learning and Applications, pp. 304–311 (2004)
Littman, M., Sutton, R., Singh, S.: Predictive representations of state. In: Advances in Neural Information Processing Systems, NIPS (2002)
McCallum, A.K.: Reinforcement Learning with Selective Perception and Hidden State. Ph.D. thesis, University of Rochester (1996)
McCracken, P., Bowling, M.: Online discovery and learning of predictive state representations. In: Neural Information Processing Systems, vol. 18 (2006)
Nguyen, P., Sunehag, P., Hutter, M.: Feature reinforcement learning in practice. Tech. rep. (2011)
Poupart, P., Vlassis, N.: Model-based bayesian reinforcement learning in partially observable domains. In: Tenth International Symposium on Artificial Intelligence and Mathematics, ISAIM (2008)
Rafols, E.J., Ring, M., Sutton, R., Tanner, B.: Using predictive representations to improve generalization in reinforcement learning. In: IJCAI (2005)
Rosencrantz, M., Gordon, G.J., Thrun, S.: Learning low dimensional predictive representations. In: Proceedings ICML (2004)
Ross, S., Pineau, J., Chaib-draa, B., Kreitmann, P.: A Bayesian approach for learning and planning in partially observable Markov decision processes. Journal of Machine Learning Research 12, 1655–1696 (2011)
Singh, S., James, M., Rudary, M.: Predictive state representations: A new theory for modeling dynamical systems. In: Proceedings UAI (2004)
Soni, V., Singh, S.: Abstraction in predictive state representations. In: AAAI (2007)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. The MIT Press (1998)
Talvitie, E., Singh, S.: Simple local models for complex dynamical systems. In: Advances in Neural Information Processing Systems, NIPS (2008)
Veness, J., Ng, K.S., Hutter, M., Uther, W., Silver, D.: A Monte-Carlo AIXI approximation. JAIR 40, 95–142 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ong, S.C.W., Grinberg, Y., Pineau, J. (2012). Goal-Directed Online Learning of Predictive Models. In: Sanner, S., Hutter, M. (eds) Recent Advances in Reinforcement Learning. EWRL 2011. Lecture Notes in Computer Science(), vol 7188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29946-9_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-29946-9_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29945-2
Online ISBN: 978-3-642-29946-9
eBook Packages: Computer ScienceComputer Science (R0)