Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processes

Abstract

We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings. Our new framework correctly models the joint uncertainty in the latent parameters and the state space. We also replace the original Gaussian Process-based model with a Bayesian Neural Network, enabling more scalable inference. Thus, we expand the scope of the HiP-MDP to applications with higher dimensions and more complex dynamics.

To appear at NIPS 2017, selected for an oral presentation. 17 pages (incl references and appendix). Example code can be found at http://github.com/dtak/hip-mdp-public