Available Projects (2027)

Vision-LiDAR fusion for autonomous racing – Read more

Comparing partial and fully end-to-end reinforcement learning pipelines for autonomous racing – Read more

Using reinforcement learning to improve human learning in arcade games – Read more
Co-supervisor: Herman Kamper

Diplomacy using reinforcement learning – Read more

Gaussian belief space planning using probabilistic graphical models – Read more
Co-supervisor: Corné van Daalen

Current Students

Mike Browne (PhD)
The degenerate Kalman filter

Genn Echun (MEng)
Comparing model-based reinforcement learning and model predictive control for multi-vehicle racing

Nico Martin (MEng)
Main supervisor: Lijun Zhang
Safe reinforcement learning for autonomous racing

Past Students
2026

Chris Flood (MEng)
Main supervisor: Herman Engelbrecht
Developing and testing a full-stack system for F1tenth autonomous racing

2025

Emile Visser (PhD)
Co-supervisor: Corné van Daalen
LABCAT: Locally adaptive Bayesian optimization using principal-component-aligned trust regions – View dissertation

Francois Bredell (PhD)
Co-supervisor: Herman Engelbrecht
Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi

Devin Jefferies (MEng)
Co-supervisor: Benjamin Evans
Autonomous racing on unseen tracks using reinforcement learning – View thesis

Stephano Buys (MEng)
Co-supervisor: Japie Engelbrecht
Cooperative search and rescue using a scheduling algorithm

2023

Andrew Murdoch (MEng)
Co-supervisor: Willem Jordaan
Partial end-to-end reinforcement learning for robustness towards model-mismatch in autonomous racing – View thesis

Ulrich Louw (MEng)
Main supervisor: Willem Jordaan
Autonomous Diagnosis of Satellite Sensor Anomalies to Ensure Fault Tolerant Control – View thesis

Welri Botes (MEng)
Main supervisor: Japie Engelbrecht
Grid-Based Coverage Path Planning for Multiple UAVs in Search and Rescue Applications – View thesis

2021

Cobus Louw (MEng)
Main supervisor: Herman Engelbrecht
Solving Sparse-reward Problems in Partially Observable 3D Environments using Distributed Reinforcement Learning – View thesis