Welcome to my home page. I am Associate Professor at the Interactive Intelligence group at TU Delft. Additionally, you might be interested in the following:

My main research interests lie in what I call interactive learning and decision making: the intersection of AI, machine learning and game theory that focuses on an intelligent agent that interacts with a complex world. My long term vision is the construction of a collaborative AI scientist. In the short term, I try to generate fundamental knowledge about algorithms and models for learning complex tasks. Specifically, I believe that agents need models to support intelligent decision making. Learning such models is difficult, and given that our world constantly changes, we cannot assume that  agents will ever learn perfect models. Instead, we need to endow them with the capability to learn these models online, i.e., while interacting with their environments: they need to be able to use imperfect models, reason about the uncertainty in their predictions, and actively learn to improve these models (balancing task rewards and knowledge gathering). In addition, I think about how such abstract models might be applied to a variety of real-world tasks such as collaboration in multi-robot or human-AI systems, optimization of traffic control systems, intelligent e-commerce agents, etc.

For more information about my research, look here.

For research opportunities, look here.

For more information about possible student projects (current TU Delft students), look here.


  • June 5th, 2024:    Looking for PhD student

    With Julia Olkhovskaia as the main supervisor, I am looking for a (fully paid) PhD student.

    See details on my vacancy page.

  • February 20th, 2023:    Wanted: assist./assoc. professor in causal reinforcement learning

    At TU Delft, we are recruiting an assist/assoc. professor in causal reinforcement learning.

    More info here.

  • December 2nd, 2022:    Comments for Volkskrant

    For the Volkskrant, I commented on the Stratego article in science. Read it here.

  • September 24th, 2022:    Berkeley MARL Seminar talk online

    The talk that I gave for the Berkeley MARL seminar can now be seen on youtube.

    It gives an introduction to ideas of influence-based abstraction, focusing also on the inspirations from multiagent planning, as well as implications for future MARL.

  • September 15th, 2022:    DIALS accepted to NeurIPS’22

    Our paper Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems was accepted to NeurIPS! It shows how influence-based abstraction can be used to parallelize and thus speed up multiagent reinforcement learning, while stabilizing the learning at the same time.

  • June 30th, 2022:    2 ILDM papers to appear at ICML

    Our group will be presenting 2 papers at ICML’22:

    Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep RL in Large Networked Systems.

    On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games

    Come find us at ICML, or reach out over email!

  • April 20th, 2022:    ILDM@AAMAS

    There are 4 ILDM papers that will be presented at the main AAMAS conference. Here is the schedule in CEST:

    MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active Learning
    Markus Peschl, Arkady Zgonnikov, Frans Oliehoek and Luciano Siebert
    1A2-2 – CEST (UTC +2) Wed, 11 May 2022 18:00
    2C5-1 – CEST (UTC +2) Thu, 12 May 2022 09:00

    Best-Response Bayesian Reinforcement Learning with BA-POMDPs for Centaurs
    Mustafa Mert Çelikok, Frans A. Oliehoek and Samuel Kaski
    2C2-2 – CEST (UTC +2) Thu, 12 May 2022 10:00
    2A4-3 – CEST (UTC +2) Thu, 12 May 2022 20:00

    LEARN BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs
    Sammie Katt, Hai Nguyen, Frans Oliehoek and Christopher Amato
    1A2-2 – CEST (UTC +2) Wed, 11 May 2022 18:00
    3B3-2 – CEST (UTC +2) Fri, 13 May 2022 03:00

    Miguel Suau, Jinke He, Matthijs Spaan and Frans Oliehoek
    Speeding up Deep Reinforcement Learning through Influence-Augmented Local Simulators
    Poster session PDC2 – CEST (UTC +2) Thu, 12 May 2022 12:00

    Poincaré-Bendixson Limit Sets in Multi-Agent Learning (Best paper runner-up)
    Aleksander Czechowski and Georgios Piliouras
    1A4-1 11th May 5pm CEST
    3C1-1 13th May 9am CEST

  • April 20th, 2022:    Senior Member AAAI

    I was elected as a senior member of the association for the advancement of artificial intelligence (AAAI). The senior member status recognizes “AAAI members who have achieved significant accomplishments within the field of artificial intelligence”. I thank my nominators and the committee for this great honor.


  • March 2nd, 2022:    First place ILDM team in RangL Pathways to Net Zero challenge

    Aleksander Czechowski and Jinke He are one of the winning teams (‘Epsilon-greedy’) of The RangL Pathways to Net Zero challenge!

    The challenge was to find the optimal pathway to a carbon neutral 2050. ‘RangL’ is a competition platform created at The Alan Turing Institute as a new model of collaboration between academia and industry. RangL offers an AI competition environment for practitioners to apply classical and machine learning techniques and expert knowledge to data-driven control problems.

    More information: https://rangl.org/blog/ and https://github.com/rangl-labs/netzerotc.

  • January 26th, 2022:    AAMAS’22 paper: Bayesian RL to cooperate with humans

    In our new paper Best-Response Bayesian Reinforcement Learning with BA-POMDPs for Centaurs, we investigate a machine whose actions can be overridden by the human. We show how Bayesian RL might lead to quick adaptation to unknown human preferences, as well as aiding the human to pursue its true goals in case of temporally inconsistent behaviors. All credits to Mert for all the hard work!

Old news