  We propose a multi-time-scale predictive representation learning method to
efficiently learn robust driving policies in an offline manner that generalize
well to novel road geometries, and damaged and distracting lane conditions
which are not covered in the offline training data. We show that our proposed
representation learning method can be applied easily in an offline (batch)
reinforcement learning setting demonstrating the ability to generalize well and
efficiently under novel conditions compared to standard batch RL methods. Our
proposed method utilizes training data collected entirely offline in the
real-world which removes the need of intensive online explorations that impede
applying deep reinforcement learning on real-world robot training. Various
experiments were conducted in both simulator and real-world scenarios for the
purpose of evaluation and analysis of our proposed claims.
