An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward

Matthew Hoffman, Nando Freitas, Arnaud Doucet, Jan Peters
; Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:232-239, 2009.

Abstract

We derive a new expectation maximization algorithm for policy optimization in linear Gaussian Markov decision processes, where the reward function is parameterised in terms of a flexible mixture of Gaussians. This approach exploits both analytical tractability and numerical optimization. Consequently, on the one hand, it is more flexible and general than closed-form solutions, such as the widely used linear quadratic Gaussian (LQG) controllers. On the other hand, it is more accurate and faster than optimization methods that rely on approximation and simulation. Partial analytical solutions (though costly) eliminate the need for simulation and, hence, avoid approximation error. The experiments will show that for the same cost of computation, policy optimization methods that rely on analytical tractability have higher value than the ones that rely on simulation.

Cite this Paper


BibTeX
@InProceedings{pmlr-v5-hoffman09a, title = {An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward}, author = {Matthew Hoffman and Nando Freitas and Arnaud Doucet and Jan Peters}, booktitle = {Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics}, pages = {232--239}, year = {2009}, editor = {David van Dyk and Max Welling}, volume = {5}, series = {Proceedings of Machine Learning Research}, address = {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v5/hoffman09a/hoffman09a.pdf}, url = {http://proceedings.mlr.press/v5/hoffman09a.html}, abstract = {We derive a new expectation maximization algorithm for policy optimization in linear Gaussian Markov decision processes, where the reward function is parameterised in terms of a flexible mixture of Gaussians. This approach exploits both analytical tractability and numerical optimization. Consequently, on the one hand, it is more flexible and general than closed-form solutions, such as the widely used linear quadratic Gaussian (LQG) controllers. On the other hand, it is more accurate and faster than optimization methods that rely on approximation and simulation. Partial analytical solutions (though costly) eliminate the need for simulation and, hence, avoid approximation error. The experiments will show that for the same cost of computation, policy optimization methods that rely on analytical tractability have higher value than the ones that rely on simulation.} }
Endnote
%0 Conference Paper %T An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward %A Matthew Hoffman %A Nando Freitas %A Arnaud Doucet %A Jan Peters %B Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2009 %E David van Dyk %E Max Welling %F pmlr-v5-hoffman09a %I PMLR %J Proceedings of Machine Learning Research %P 232--239 %U http://proceedings.mlr.press %V 5 %W PMLR %X We derive a new expectation maximization algorithm for policy optimization in linear Gaussian Markov decision processes, where the reward function is parameterised in terms of a flexible mixture of Gaussians. This approach exploits both analytical tractability and numerical optimization. Consequently, on the one hand, it is more flexible and general than closed-form solutions, such as the widely used linear quadratic Gaussian (LQG) controllers. On the other hand, it is more accurate and faster than optimization methods that rely on approximation and simulation. Partial analytical solutions (though costly) eliminate the need for simulation and, hence, avoid approximation error. The experiments will show that for the same cost of computation, policy optimization methods that rely on analytical tractability have higher value than the ones that rely on simulation.
RIS
TY - CPAPER TI - An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward AU - Matthew Hoffman AU - Nando Freitas AU - Arnaud Doucet AU - Jan Peters BT - Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics PY - 2009/04/15 DA - 2009/04/15 ED - David van Dyk ED - Max Welling ID - pmlr-v5-hoffman09a PB - PMLR SP - 232 DP - PMLR EP - 239 L1 - http://proceedings.mlr.press/v5/hoffman09a/hoffman09a.pdf UR - http://proceedings.mlr.press/v5/hoffman09a.html AB - We derive a new expectation maximization algorithm for policy optimization in linear Gaussian Markov decision processes, where the reward function is parameterised in terms of a flexible mixture of Gaussians. This approach exploits both analytical tractability and numerical optimization. Consequently, on the one hand, it is more flexible and general than closed-form solutions, such as the widely used linear quadratic Gaussian (LQG) controllers. On the other hand, it is more accurate and faster than optimization methods that rely on approximation and simulation. Partial analytical solutions (though costly) eliminate the need for simulation and, hence, avoid approximation error. The experiments will show that for the same cost of computation, policy optimization methods that rely on analytical tractability have higher value than the ones that rely on simulation. ER -
APA
Hoffman, M., Freitas, N., Doucet, A. & Peters, J.. (2009). An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward. Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, in PMLR 5:232-239

Related Material