Q-learning with Long-term Action-space Shaping to Model Complex Behavior for Autonomous Lane Changes

Abstract

In autonomous driving applications, reinforcement learning agents often have to perform complex behavior, which can translate into optimizing multiple objectives while following certain rules. Encoding traffic rules and desires such as safety and comfort via classical methods based on reward shaping (i.e. a weighted combination of different objectives in the reward signal) or Lagrangian methods (including auxiliary losses in the optimization) can be very hard and cumbersome. In this work, we propose to instead shape the action-space at the maximization step of Q-learning. We further introduce a formulation for fixed-horizon estimation of auxiliary costs under the current target-policy based on truncated value- functions to encode the desire of comfortable driving ensuring interpretable behavior. We compare our algorithm to reward shaping and Lagrangian methods in the application of high- level decision making in autonomous driving, considering rules for safety, keeping right and comfort. We train and evaluate our agent in the open-source simulator SUMO on a variety of scenarios with different driver types and traffic situations. Additionally, we apply our method on the real HighD data set, showing the real-world applicability and simplicity of Q- learning with Action-space Shaping.