Hierarchical Relative Entropy Policy Search
; Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:273-281, 2012.
Many real-world problems are inherently hi- erarchically structured. The use of this struc- ture in an agent’s policy may well be the key to improved scalability and higher per- formance. However, such hierarchical struc- tures cannot be exploited by current policy search algorithms. We will concentrate on a basic, but highly relevant hierarchy - the ’mixed option’ policy. Here, a gating network first decides which of the options to execute and, subsequently, the option-policy deter- mines the action. In this paper, we reformulate learning a hi- erarchical policy as a latent variable estima- tion problem and subsequently extend the Relative Entropy Policy Search (REPS) to the latent variable case. We show that our Hierarchical REPS can learn versatile solu- tions while also showing an increased perfor- mance in terms of learning speed and quality of the found policy in comparison to the non- hierarchical approach.