Investigating Value of Curriculum Reinforcement Learning in Autonomous
  Driving Under Diverse Road and Weather Conditions
  Applications of reinforcement learning (RL) are popular in autonomous driving
tasks. That being said, tuning the performance of an RL agent and guaranteeing
the generalization performance across variety of different driving scenarios is
still largely an open problem. In particular, getting good performance on
complex road and weather conditions require exhaustive tuning and computation
time. Curriculum RL, which focuses on solving simpler automation tasks in order
to transfer knowledge to complex tasks, is attracting attention in RL
community. The main contribution of this paper is a systematic study for
investigating the value of curriculum reinforcement learning in autonomous
driving applications. For this purpose, we setup several different driving
scenarios in a realistic driving simulator, with varying road complexity and
weather conditions. Next, we train and evaluate performance of RL agents on
different sequences of task combinations and curricula. Results show that
curriculum RL can yield significant gains in complex driving tasks, both in
terms of driving performance and sample complexity. Results also demonstrate
that different curricula might enable different benefits, which hints future
research directions for automated curriculum training.
