TR2012-083
Markov Decision Processes for Train Run Curve Optimization
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- "Markov Decision Processes for Train Run Curve Optimization", Electrical Systems for Aircraft, Railway and Ship Propulsion (ESARS), DOI: 10.1109/ESARS.2012.6387473, ISSN: 2165-9400, ISBN: 978-1-4673-1370-4, October 2012, pp. 1-6.BibTeX Download PDF
- @inproceedings{Nikovski2012oct,
- author = {Nikovski, D. and Lidicky, B. and Zhang, W. and Kataoka, K. and Yoshimoto, K.},
- title = {Markov Decision Processes for Train Run Curve Optimization},
- booktitle = {Electrical Systems for Aircraft, Railway and Ship Propulsion (ESARS)},
- year = 2012,
- pages = {1--6},
- month = oct,
- doi = {10.1109/ESARS.2012.6387473},
- issn = {2165-9400},
- isbn = {978-1-4673-1370-4},
- url = {http://www.merl.com/publications/TR2012-083}
- }
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- "Markov Decision Processes for Train Run Curve Optimization", Electrical Systems for Aircraft, Railway and Ship Propulsion (ESARS), DOI: 10.1109/ESARS.2012.6387473, ISSN: 2165-9400, ISBN: 978-1-4673-1370-4, October 2012, pp. 1-6.
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We propose three computationally efficient methods for finding optimal run curves of electrical trains, all based on the idea of approximating the continuous dynamics of a moving train by a Markov Decision Process (MDP) model. Deterministic continuous train dynamics are converted to stochastic transitions on a discrete model by observing the similarity between the properties of convex combinations and those of probability mass functions. The resulting MDP uses barycentric coordinates to effectively represent the cost-to-go of the approximated optimal control problem. One of the three solution methods uses equal distance steps, as opposed to the usual equal- time steps, to avoidself transitions of the MDP, which allows very fast computation of the cost-to-go in one pass only.