Title: Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States,
Abstract: Making the right decision in traffic is a challenging task that is highly
dependent on individual preferences as well as the surrounding environment.
Therefore it is hard to model solely based on expert knowledge. In this work we
use Deep Reinforcement Learning to learn maneuver decisions based on a compact
semantic state representation. This ensures a consistent model of the
environment across scenarios as well as a behavior adaptation function,
enabling on-line changes of desired behaviors without re-training. The input
for the neural network is a simulated object list similar to that of Radar or
Lidar sensors, superimposed by a relational semantic scene description. The
state as well as the reward are extended by a behavior adaptation function and
a parameterization respectively. With little expert knowledge and a set of
mid-level actions, it can be seen that the agent is capable to adhere to
traffic rules and learns to drive safely in a variety of situations.