Effcient inference in Markov control problems

Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011

Volumn , Issue , 2011, Pages 221-229

  Furmston, Thomas ^a   Barber, David ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

CONTROL PROBLEMS; EXACT INFERENCE; EXPECTATION-MAXIMISATION; FINITE HORIZONS; INFINITE HORIZON PROBLEMS; INFINITE HORIZONS; MARKOV DECISION PROBLEM; NOVEL ALGORITHM;

INFERENCE ENGINES;

EID: 80053139999     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. D. Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press, 2011.
2. P. Dayan and G. E. Hinton. Using Expectation-Maximization for Reinforcement Learning. Neural Computation, 9:271-278, 1997. (Pubitemid 127635391)
3. T. Furmston and D. Barber. Variational Methods for Reinforcement Learning. AISTATS, 9(13):241-248, 2010.
4. M. Hoffman, A. Doucet, N. de Freitas, and A. Jasra. Trans-dimensional MCMC for Bayesian Policy Learning. NIPS, 20:665-672, 2008.
5. M. Hoffman, N. de Freitas, A. Doucet, and J. Peters. An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Rewards. AISTATS, 5(12):232-239, 2009.
6. L. Kaelbling, M. Littman, and A. Cassandra. Planning and Acting in Partially Observable Stochastic Domains. Artificial Intelligence, 101:99-134, 1998. (Pubitemid 128387390)
7. H. Khalil. Nonlinear Systems. Prentice Hall, 2001.
8. J. Kober and J. Peters. Policy search for motor primitives in robotics. NIPS, 21:849-856, 2009.
9. J. Peters and S. Schaal. Policy Gradient Methods for Robotics. IROS, 21:2219-2225, 2006. (Pubitemid 46928224)
10. R. Salakhutdinov, S. Roweis, and Z. Ghahramani. Optimization with EM and Expectation-Conjugate-Gradient. ICML, (20):672-679, 2003.
11. M. Spong, S. Hutchinson, and M. Vidyasagar. Robot Modelling and Control. John Wiley & Sons, 2005.
12. R. Sutton, D. McAllester, S. Singh, and Y. Mansour. Policy Gradient Methods for Reinforcement Learning with Function Approximation. NIPS, 13, 2000.
13. R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. MIT Press, 1998.
14. M. Toussaint. Pros and Cons of truncated Gaussian EP in the context of Approximate Inference Control. NIPS - Workshop on Probabilistic Approaches for Robotics and Control., 21, 2009.
15. M. Toussaint, S. Harmeling, and A. Storkey. Probabilistic inference for solving (PO)MDPs. Research Report EDI-INF-RR-0934, University of Edinburgh, School of Informatics, 2006.
16. M. Toussaint, A. Storkey, and S. Harmeling. Bayesian Time Series Models, chapter Expectation-Maximization methods for solving (PO)MDPs and optimal control problems. Cambridge University Press, 2011. In press. See userpage.fu-berlin.de/~mtoussai.
17. M. J. Wainwright and M. I. Jordan. Graphical Models, Exponential Families, and Variational Inference. Foundations and Trends in Machine Learning, 1(1-2):1-305, 2008.