On Stochastic Optimal Control and Reinforcement Learning by Approximate Inference (Extended Abstract)
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 3052-3055.
Abstract
We present a reformulation of the stochastic optimal control problem in terms of KL divergence minimisation, not only providing a unifying perspective of previous approaches in this area, but also demonstrating that the formalism leads to novel practical approaches to the control problem. Specifically, a natural relaxation of the dual formulation gives rise to exact iterative solutions to the finite and infinite horizon stochastic optimal control problem, while direct application of Bayesian inference methods yields instances of risk sensitive control.
@inproceedings{ijcai2013p455,
title = {On Stochastic Optimal Control and Reinforcement Learning by Approximate Inference (Extended Abstract)},
author = {Rawlik, Konrad and Toussaint, Marc and Vijayakumar, Sethu},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {3052--3055},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/455},
}
TY - CPAPER TI - On Stochastic Optimal Control and Reinforcement Learning by Approximate Inference (Extended Abstract) AU - Rawlik, Konrad AU - Toussaint, Marc AU - Vijayakumar, Sethu T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 3052 EP - 3055 UR - https://ijcai.org/proceedings/2013/455 ER -Copied
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