Abstract:
The simplicity of Approximate Dynamic Programming offers benefits for large-scale systems compared to other synthesis and control methodologies. A common technique to app...Show MoreMetadata
Abstract:
The simplicity of Approximate Dynamic Programming offers benefits for large-scale systems compared to other synthesis and control methodologies. A common technique to approximate the Dynamic Program, is through the solution of the corresponding Linear Program. The major drawback of this approach is that the online performance is very sensitive to the choice of tuning parameters, in particular the state relevance weighting parameter. Our work aims at alleviating this sensitivity. To achieve this, we propose to find a set of approximate Q-functions, each for a different choice of the tuning parameters, and then to use the pointwise maximum of the set of Q-functions for the online policy. The pointwise maximum promises to be better than using only one of individual Q-functions for the online policy. We demonstrate that this approach immunizes against tuning errors through a stylized portfolio optimization problem.
Published in: 2016 European Control Conference (ECC)
Date of Conference: 29 June 2016 - 01 July 2016
Date Added to IEEE Xplore: 09 January 2017
ISBN Information: