  Self-driving vehicles must be able to act intelligently in diverse and
difficult environments, marked by high-dimensional state spaces, a myriad of
optimization objectives and complex behaviors. Traditionally, classical
optimization and search techniques have been applied to the problem of
self-driving; but they do not fully address operations in environments with
high-dimensional states and complex behaviors. Recently, imitation learning has
been proposed for the task of self-driving; but it is labor-intensive to obtain
enough training data. Reinforcement learning has been proposed as a way to
directly control the car, but this has safety and comfort concerns. We propose
using model-free reinforcement learning for the trajectory planning stage of
self-driving and show that this approach allows us to operate the car in a more
safe, general and comfortable manner, required for the task of self driving.
