A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States
[article]
2018
arXiv
pre-print
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
arXiv:1809.03214v1
fatcat:u55npj4ot5ffni7edmcfs373uq