Title: Learning Robust Options,
Abstract: Robust reinforcement learning aims to produce policies that have strong
guarantees even in the face of environments/transition models whose parameters
have strong uncertainty. Existing work uses value-based methods and the usual
primitive action setting. In this paper, we propose robust methods for learning
temporally abstract actions, in the framework of options. We present a Robust
Options Policy Iteration (ROPI) algorithm with convergence guarantees, which
learns options that are robust to model uncertainty. We utilize ROPI to learn
robust options with the Robust Options Deep Q Network (RO-DQN) that solves
multiple tasks and mitigates model misspecification due to model uncertainty.
We present experimental results which suggest that policy iteration with linear
features may have an inherent form of robustness when using coarse feature
representations. In addition, we present experimental results which demonstrate
that robustness helps policy iteration implemented on top of deep neural
networks to generalize over a much broader range of dynamics than non-robust
policy iteration.