Meta-Reinforcement Learning for Adaptive Autonomous Driving
Abstract
Reinforcement learning (RL) methods achieved major advances in multiple tasks surpassing human performance. However, most of RL strategies show a certain degree of weakness and may become computationally intractable when dealing with high-dimensional and non-stationary environments. In this paper, we build a meta-reinforcement learning (MRL) method embedding an adaptive neural network (NN) controller for efficient policy iteration in changing task conditions. Our main goal is to extend RL application to the challenging task of urban autonomous driving in CARLA simulator.
Cite
Text
Jaafra et al. "Meta-Reinforcement Learning for Adaptive Autonomous Driving." ICML 2019 Workshops: AMTL, 2019.Markdown
[Jaafra et al. "Meta-Reinforcement Learning for Adaptive Autonomous Driving." ICML 2019 Workshops: AMTL, 2019.](https://mlanthology.org/icmlw/2019/jaafra2019icmlw-metareinforcement/)BibTeX
@inproceedings{jaafra2019icmlw-metareinforcement,
title = {{Meta-Reinforcement Learning for Adaptive Autonomous Driving}},
author = {Jaafra, Yesmina and Laurent, Jean Luc and Deruyver, Aline and Naceur, Mohamed Saber},
booktitle = {ICML 2019 Workshops: AMTL},
year = {2019},
url = {https://mlanthology.org/icmlw/2019/jaafra2019icmlw-metareinforcement/}
}