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Safe and Efficient Reinforcement Learning for Behavioural Planning in Autonomous Driving

Social Attention Risk Awareness Robust Control

This repository holds the LaTeX code of my PhD Thesis.

Build LaTeX

Publications in this thesis

Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs
Edouard Leurent, Denis Efimov, and Odalric-Ambrym Maillard
NeurIPS 2020
Paper Code
Monte-Carlo Graph Search: the Value of Merging Similar States
Edouard Leurent and Odalric-Ambrym Maillard
ACML 2020
Paper Code
Robust-Adaptive Interval Predictive Control for Linear Uncertain Systems
Edouard Leurent, Denis Efimov, and Odlaric-Ambrym Maillard
CDC 2020
Paper Code
Budgeted Reinforcement Learning in Continuous State Space
Nicolas Carrara,* Edouard Leurent,* Romain Laroche, Tanguy Urvoy, Odalric-Ambrym Maillard, and Olivier Pietquin
NeurIPS 2019
Paper Code
Social Attention for Autonomous Decision-Making in Dense Traffic
Edouard Leurent and Jean Mercat
ML4AD Workshop at NeurIPS 2019
Paper Code
Interval Prediction for Continuous-Time Systems with Parametric Uncertainties
Edouard Leurent, Denis Efimov, Tarek Raïssi, and Wilfrid Perruquetti
CDC 2019
Paper Code
Practical Open-Loop Optimistic Planning
Edouard Leurent and Odalric-Ambrym Maillard
ECML-PKDD 2019
Paper Code
* Equal contribution.

Defense

I defended on the 30th of October 2020, in front of the jury composed of:

  • Lucian Buşoniu
  • Jorge Villagra
  • Luce Brotcorne
  • Marc Deisenroth
  • Denis Efimov
  • Odalric-Ambrym Maillard

The slides 📰 that I used are also available online, and open-source.

How to build the PDF?

I recommend using latexmk to build this project.

latexmk -pdf PhD_thesis__Edouard_Leurent.tex

It is used e.g. in the latex-action of the automatic build workflow. LaTeX editors such as Texstudio can be configured to use latexmk as the default compiler.

Credits

The template is inspired from @Naereen/phd-thesis, also released under the MIT License.