Marc Rigter

Welcome to my personal website!

I’m currently a machine learning researcher in the Causica team at Microsoft Research, working on improving the decision-making capabilities of foundation models.

Previously I was a postdoc at the Applied AI Lab at the Oxford Robotics Institute (ORI) at the University of Oxford. I completed my PhD in Information Engineering at the GOALS Group also at the ORI. At Oxford, my research focused on generative “world” models, model-based reinforcement learning, and planning under uncertainty.

Please feel free to reach out if you would like to chat about anything!

Photo Collections

New Zealand / Aotearoa

United Kingdom

United States

Australia

Europe

South East Asia

Wildlife

Selected Publications

ICLR

Reward-free curricula for training robust world models

Marc Rigter , Minqi Jiang , and Ingmar Posner

International Conference on Learning Representations, 2024

Bib PDF Code

@article{rigter2024reward,
  title = {Reward-free curricula for training robust world models},
  author = {Rigter, Marc and Jiang, Minqi and Posner, Ingmar},
  journal = {International Conference on Learning Representations},
  year = {2024},
}

TMLR

World models via policy-guided trajectory diffusion

Marc Rigter , Jun Yamada , and Ingmar Posner

Transactions on Machine Learning Research, 2024

Bib PDF Code

@article{rigter2023world,
  title = {World models via policy-guided trajectory diffusion},
  author = {Rigter, Marc and Yamada, Jun and Posner, Ingmar},
  journal = {Transactions on Machine Learning Research},
  year = {2024},
}

NeurIPS

One risk to rule them all: A risk-sensitive perspective on model-based offline reinforcement learning

Marc Rigter , Bruno Lacerda , and Nick Hawes

Advances in Neural Information Processing Systems, 2023

Bib PDF Code

@article{rigter2023one,
  title = {One risk to rule them all: {A} risk-sensitive perspective on model-based offline reinforcement learning},
  author = {Rigter, Marc and Lacerda, Bruno and Hawes, Nick},
  journal = {Advances in Neural Information Processing Systems},
  year = {2023},
}

NeurIPS

RAMBO-RL: Robust adversarial model-based offline reinforcement learning

Marc Rigter , Bruno Lacerda , and Nick Hawes

Advances in Neural Information Processing Systems, 2022

Bib PDF Code

@article{rigter2022rambo,
  title = {{RAMBO-RL}: Robust adversarial model-based offline reinforcement learning},
  author = {Rigter, Marc and Lacerda, Bruno and Hawes, Nick},
  journal = {Advances in Neural Information Processing Systems},
  year = {2022},
}

AAAI

Optimal admission control for multiclass queues with time-varying arrival rates via state abstraction

Marc Rigter , Danial Dervovic , Parisa Hassanzadeh , and 3 more authors

AAAI Conference on Artificial Intelligence, 2022

Bib PDF

@article{rigter2022optimal,
  title = {Optimal admission control for multiclass queues with time-varying arrival rates via state abstraction},
  author = {Rigter, Marc and Dervovic, Danial and Hassanzadeh, Parisa and Long, Jason and Zehtabi, Parisa and Magazzeni, Daniele},
  journal = {AAAI Conference on Artificial Intelligence},
  year = {2022},
}

ICAPS

Planning for risk-aversion and expected value in MDPs

Marc Rigter , Paul Duckworth , Bruno Lacerda , and 1 more author

International Conference on Automated Planning and Scheduling, 2022

Bib PDF Code

@article{rigter2022planning,
  title = {Planning for risk-aversion and expected value in MDPs},
  author = {Rigter, Marc and Duckworth, Paul and Lacerda, Bruno and Hawes, Nick},
  journal = {International Conference on Automated Planning and Scheduling},
  year = {2022},
}

NeurIPS

Risk-averse Bayes-adaptive reinforcement learning

Marc Rigter , Bruno Lacerda , and Nick Hawes

Advances in Neural Information Processing Systems, 2021

Bib PDF

@article{rigter2021risk,
  title = {Risk-averse {B}ayes-adaptive reinforcement learning},
  author = {Rigter, Marc and Lacerda, Bruno and Hawes, Nick},
  journal = {Advances in Neural Information Processing Systems},
  year = {2021},
}

AAAI

Minimax regret optimisation for robust planning in uncertain Markov decision processes

Marc Rigter , Bruno Lacerda , and Nick Hawes

AAAI Conference on Artificial Intelligence, 2021

Bib PDF

@article{rigter2021minimax,
  title = {Minimax regret optimisation for robust planning in uncertain Markov decision processes},
  author = {Rigter, Marc and Lacerda, Bruno and Hawes, Nick},
  journal = {AAAI Conference on Artificial Intelligence},
  year = {2021},
}

RAL

A framework for learning from demonstration with minimal human effort

Marc Rigter , Bruno Lacerda , and Nick Hawes

IEEE Robotics and Automation Letters, 2020

Bib PDF

@article{rigter2020framework,
  title = {A framework for learning from demonstration with minimal human effort},
  author = {Rigter, Marc and Lacerda, Bruno and Hawes, Nick},
  journal = {IEEE Robotics and Automation Letters},
  year = {2020},
}

IROS

An autonomous quadrotor system for robust high-speed flight through cluttered environments without GPS

Marc Rigter , Benjamin Morrell , Robert G Reid , and 5 more authors

International Conference on Intelligent Robots and Systems, 2019

Bib PDF

@article{rigter2019autonomous,
  title = {An autonomous quadrotor system for robust high-speed flight through cluttered environments without GPS},
  author = {Rigter, Marc and Morrell, Benjamin and Reid, Robert G and Merewether, Gene B and Tzanetos, Theodore and Rajur, Vinay and Wong, KC and Matthies, Larry H},
  journal = {International Conference on Intelligent Robots and Systems},
  year = {2019},
}