Thomas Moerland
Assistant professor
- Name
- Dr. T.M. Moerland
- Telephone
- +31 71 527 4799
- t.m.moerland@liacs.leidenuniv.nl
Thomas Moerland is an Assistant Professor at the computer science institute (LIACS). His research focuses on artificial intelligence, and he also teaches in this field in both bachelor and master programmes. In 2025, he published the (Dutch) book Van IQ tot AI, which explores the relationship between artificial intelligence and human thought.
Thomas Moerland studied mathematics and medicine at Leiden University. Afterwards, he obtained his PhD in computer science at Delft University of Technology, specializing in artificial intelligence. His research primarily focuses on reinforcement learning, the branch of machine learning in which computers learn to make decisions through reward and punishment ('trial and error'). After completing his PhD, he returned to Leiden University, where he leads the Reinforcement Learning Group.
In addition to his more technical research, he also has a broader interest in the mechanisms of intelligence — both in machines and in the human brain. He wrote the (Dutch) book Van IQ Naar AI, which explores the similarities, differences, and mutual influences between both worlds. He also actively engages in communication about AI to the general public, from the belief that safe use of AI starts with proper understanding of the underlying technology. Please visit his personal website for further information.
Assistant professor
- Faculty of Science
- Leiden Inst of Advanced Computer Science
- Moerland T.M., Broekens D.J., Plaat A. & Jonker C.M. (2023), Model-based reinforcement learning: a survey, Foundations and Trends in Machine Learning 16(1): 1-118.
- Moerland T.M., Broekens D.J., Plaat A. & Jonker C.M. (2022), A unifying framework for reinforcement learning and planning, Frontiers in Artificial Intelligence 5: 908353.
- Moerland T.M. (2025), Van IQ Naar AI. Amsterdam: Atlas Contact.
- Ponse K., Plaat A., Stein N. van & Moerland T.M. (2025), EconoJax: a fast & scalable economic simulation in jax, Proceedings of the 24th international conference on autonomous agents and multiagent systems. 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025) 19 May 2025 - 23 May 2025: International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). 1679-1687.
- Yang Z., Moerland T.M., Preuss M., Plaat A. & Hu E.S. (2025), Reset-free reinforcement learning with world models, Transactions on Machine Learning Research : .
- Majellaro R., Collu J., Plaat A. & Moerland T.M. (2025), Explicitly disentangled representations in object-centric learning, Transactions on Machine Learning Research : .
- Ponse K., Kleuker J.F, Plaat A. & Moerland T.M. (2025), Chargax: a JAX accelerated EV charging simulator, Reinforcement Learning Journal 6: 363-383.
- Kleuker J.F, Plaat A. & Moerland T.M. (2025), On the effect of regularization in policy mirror descent, Reinforcement Learning Journal 6: 1931-1950.
- He J., Moerland T.M. Vries J.A. de & Oliehoek F.A. (2024), What model does MuZero learn?. Endriss U., Melo F.S., Bach K., Bugarin-Diz A., Alonso-Moral J.M., Barro S. & Heintz F. (Eds.), ECAI 2024. 27th European Conference on Artificial Intelligence (ECAI 2024) 19 October 2024 - 24 October 2024. Frontiers in Artificial Intelligence and Applications no. 392: IOS . 1599-1606.
- Renting B.M., Moerland T.M. & Hoos H.H. Jonker C.M. (2024), Towards general negotiation strategies with end-to-end reinforcement learning, Reinforcement Learning Journal. Reinforcement Learning Conference (RCL) 9 August 2024 - 12 August 2024 2059-2070.
- Huisman M., Moerland T.M., Plaat A. & Rijn J.N. van (2023), Are LSTMs good few-shot learners?, Machine Learning 112: 4635–4662.
- Yang Z., Moerland T.M., Preuss M. & Plaat A. (2023), First go, then post-explore: the benefits of post-exploration in intrinsic motivation. Rocha A.P., Steels L. & Herik J. van den (Eds.), Proceedings of the 15th international conference on agents and artificial intelligence . 15th International Conference on Agents and Artificial Intelligence 22 February 2023 - 24 February 2023 no. 2: Scitepress. 27-34.
- Yang Z., Moerland T.M., Preuss M. & Plaat A. (2023), Continuous episodic control, Proceedings of the 2023 IEEE Conference on Games, CoG 2023. 2023 IEEE Conference on Games (CoG) 21 August 2023 - 24 August 2023: IEEE Computer Society. 1-8.
- Yang Z., Moerland T.M., Preuss M. & Plaat A. (2023), Two-memory reinforcement learning, 2023 IEEE Conference on Games (CoG). 2023 IEEE Conference on Games (CoG) 21 August 2023 - 24 August 2023: IEEE. 1-9.
- Vries J.A. de, Moerland T.M. & Plaat A. (2022), On credit assignment in hierarchical reinforcement learning. . Workshop on Agent Learning in Open-Endedness at the International Conference on Learning Representations (ICLR).
- De Vries J.A., Voskuil K.S., Moerland T.M. & Plaat A. (2021), Visualizing MuZero models. 38th International Conference on Machine Learning Conference (ICML 2021) 18 July 2021 - 24 July 2021.
- Moerland T.M. (10 March 2021), The Intersection of Planning and Learning (Dissertatie, Electrical Engineering, Mathematics and Computer Science, Delft University of Technology). Supervisor(s) and Co-supervisor(s): Jonker C.M. Plaat. A., Broekens J.
- Moerland T.M., Deichler A., Baldi S., Broekens D.J. & Jonker C.M. (2020), Think too fast nor too slow: the computational trade-off between planning and reinforcement learning. Fern A., Gómez V., Jonsson A., Katz M., Palacios H. & Sanner S. (Eds.), Proceedings of the 1st workshop on bridging the gap between AI Planning and Reinforcement Learning (PRL). 30th International Conference on Automated Planning and Scheduling (ICAPS 19 October 2020 - 30 October 2020.
- WJ Wolfslag M Bharatheesha TM Moerland M Wisse (2018), RRT-CoLearn: towards kinodynamic planning without numerical trajectory optimization, IEEE Robotics and Automation Letters : .
- Moerland T.M., Broekens D.J. & Jonker C.M. (2018), Emotion in reinforcement learning agents and robots: a survey, Machine Learning 107: 443-480.
- Moerland T.M., Broekens D.J., Plaat A. & Jonker C.M. (2018), Monte Carlo tree search for asymmetric trees. Dy J. & Krause A. (Eds.), Proceedings of machine learning research. 35th International Conference on Machine Learning 10 July 2018 - 15 July 2018. Proceedings of Machine Learning Research no. 80: MLReseachPress.
- Moerland T.M., Broekens D.J., Plaat A. & Jonker C.M. (2018), A0C: Alpha zero in continuous action space. Dy J. & Krause A. (Eds.), Proceedings of machine learning research. 35th International Conference on Machine Learning 10 July 2018 - 15 July 2018. Proceedings of Machine Learning Research no. 80: MLReseachPress.
- Moerland T.M., Broekens D.J. & Jonker C.M. (2018), The potential of the return distribution for exploration in RL. ICML 2018 Workshop on Exploration in Reinforcement Learning 15 July 2018 - 15 July 2018.
- Moerland T.M., Broekens J. & Jonker C.M. (2017), Efficient exploration with Double Uncertain Value Networks. Deep Reinforcement Learning Symposium at the 30th Conference on Advances in Neural Information Processing Systems (NIPS). [conference paper].
- Moerland T.M., Broekens D.J. & Jonker C.M. (2017), Learning multimodal transition dynamics for model-based reinforcement learning. 1st Scaling-Up Reinforcement Learning (SURL) Workshop 18 September 2017 - 18 September 2017.
- TM Moerland J Broekens CM Jonker (2016), Fear and Hope Emerge from Anticipation in Model-Based Reinforcement Learning. Twenty-Fifth International Joint Conference on Artificial Intelligence 9 July 2016 - 15 July 2016.