You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
PhD Thesis: Bayesian Learning for Control in Multimodal Dynamical Systems
This repository contains the org and LaTeX source for my PhD thesis.
Publications & Code
Some of the work in my thesis is published in:
Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation
Aidan Scannell, Carl Henrik Ek, Arthur Richards Published in 2021 IEEE International Conference on Robotics and Automation (ICRA)
Mode-constrained Model-based Reinforcement Learning via Gaussian Processes
Aidan Scannell, Carl Henrik Ek, Arthur Richards Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (AISTATS)
The work in each of the content chapters is roughly split into the following code bases:
Identifiable Mixtures of Sparse Variational Gaussian Process Experts
Aidan Scannell, Carl Henrik Ek, Arthur Richards
Mode Remaining Trajectory Optimisation
Aidan Scannell, Carl Henrik Ek, Arthur Richards
Mode-constrained Model-based Reinforcement Learning via Gaussian Processes
Aidan Scannell, Carl Henrik Ek, Arthur Richards
This uses the Emacs LaTeX exporter so I provide a minimal Emacs configuration in init.el and export to pdf in batch mode.
The Dockerfile creates a working environment which can be built with
docker build -t emacs-image .
Cite
@phdthesis{scannell22,
title = {Bayesian Learning for Control in Multimodal Dynamical Systems},
author = {Aidan Scannell},
school = {University of Bristol},
year = {2022}}