Goal-driven dimensionality reduction for reinforcement learningSimone Parisi; Simon Ramstedt; Jan Peters
In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2017), September 24-28, Vancouver, BC, Canada, Pages 4634-4639, IEEE, 2017.
Defining a state representation on which optimal control can perform well is a tedious but crucial process. It typically requires expert knowledge, does not generalize straightforwardly over different tasks and strongly influences the quality of the learned controller. In this paper, we present an autonomous feature construction method for learning low-dimensional manifolds of goal-relevant features jointly with an optimal controller using reinforcement learning. Our method combines information-theoretic algorithms with principal component analysis to performs a return-weighted reduction of the state representation. The method does not require any preprocessing of the data, does not assume strong restrictions on the state representation, and substantially improves the performance of learning by reducing the number of samples required. We show that our method can learn high quality controller in redundant spaces, even from pixels, and outperforms both classical and state-of-the-art deep learning approaches.