Title: Meta learning Framework for Automated Driving,
Abstract: The success of automated driving deployment is highly depending on the
ability to develop an efficient and safe driving policy. The problem is well
formulated under the framework of optimal control as a cost optimization
problem. Model based solutions using traditional planning are efficient, but
require the knowledge of the environment model. On the other hand, model free
solutions suffer sample inefficiency and require too many interactions with the
environment, which is infeasible in practice. Methods under the Reinforcement
Learning framework usually require the notion of a reward function, which is
not available in the real world. Imitation learning helps in improving sample
efficiency by introducing prior knowledge obtained from the demonstrated
behavior, on the risk of exact behavior cloning without generalizing to unseen
environments. In this paper we propose a Meta learning framework, based on data
set aggregation, to improve generalization of imitation learning algorithms.
Under the proposed framework, we propose MetaDAgger, a novel algorithm which
tackles the generalization issues in traditional imitation learning. We use The
Open Race Car Simulator (TORCS) to test our algorithm. Results on unseen test
tracks show significant improvement over traditional imitation learning
algorithms, improving the learning time and sample efficiency in the same time.
The results are also supported by visualization of the learnt features to prove
generalization of the captured details.