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Lagrange Dual Decomposition for Finite Horizon Markov Decision Processes
This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Athens 2011. Solving finite-horizon Markov Decision Processes with stationary policies is a computationally difficult problem. Our dynamic dual decomposition approach uses Lagrange duality to decouple this hard problem into a sequence of tractable sub-problems. The resulting procedure is a straightforward modification of standard non-stationary Markov Decision Process solvers and gives an upper-bound on the total expected reward. The empirical performance of the method suggests that not only is it a rapidly convergent algorithm, but that it also performs favourably compared to standard planning algorithms such as policy gradients and lower-bound procedures such as Expectation Maximisation.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
