  Decision making for self-driving cars is usually tackled by manually encoding
rules from drivers' behaviors or imitating drivers' manipulation using
supervised learning techniques. Both of them rely on mass driving data to cover
all possible driving scenarios. This paper presents a hierarchical
reinforcement learning method for decision making of self-driving cars, which
does not depend on a large amount of labeled driving data. This method
comprehensively considers both high-level maneuver selection and low-level
motion control in both lateral and longitudinal directions. We firstly
decompose the driving tasks into three maneuvers, including driving in lane,
right lane change and left lane change, and learn the sub-policy for each
maneuver. Then, a master policy is learned to choose the maneuver policy to be
executed in the current state. All policies including master policy and
maneuver policies are represented by fully-connected neural networks and
trained by using asynchronous parallel reinforcement learners (APRL), which
builds a mapping from the sensory outputs to driving decisions. Different state
spaces and reward functions are designed for each maneuver. We apply this
method to a highway driving scenario, which demonstrates that it can realize
smooth and safe decision making for self-driving cars.
