Hierarchical Reinforcement Learning for Self-Driving Decision-Making without Reliance on Labeled Driving Data
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.