Aditya Mohan

Researcher and PhD student at the Institute of Artificial Intellifence, Hannover, working under the supervision of Prof. Marius Lindauer

prof-pic.jpg

I believe studying and explicitly utilizing information about problem structure can help us significantly improve squential decision making techniques, such as Reinforcement Learning. Thus, I am interested in exploring how agents can learn to abstract structure out of tasks (the discovery problem) and use this to improve performance.

I completed my Master’s in Autonomous Systems from Technical University of Berlin and EURECOM, where my thesis focused on ad-hoc cooperation in Hanabi. I have previously worked on problems in Robotics, Reinforcement Learning, and Meta-Learning in single-agent and Multi-Agent settings.

Apart from all of the research, I love to sing, play piano and guitar, compose music, dance and cook.

news

Apr 3, 2024 Our paper Structure in Deep Reinforcement Learning: A Survey and Open Problems has been published in the Journal of Artificial Intelligence Research (JAIR). Super thankful to my coauthors Amy Zhang and Marius Lindauer!
Mar 21, 2024 Our poster “Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization” has been accepted to The Genetic and Evolutionary Computation Conference (GECCO 2024). Do come by!
Feb 12, 2024 Our paper AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks has been accepted to the journal on Transactions on Machine Learning Research (TMLR). Check it out if you are interested in learning how AutoML and LLMs compliment each other!
Jan 11, 2024 Raghu Rajan, Theresa Eimer, Andre Bidenkapp and I have written a blog post on the AutoRL blog about the year 2023 in AutoRL research. You can read it here.
Sep 13, 2023 I will be attending EWRL 2023 and presenting the following papers: Additional, Theresa Eimer will present our work Contextualize Me – The Case for Context in Reinforcement Learning
May 31, 2023 Thrilled to have the following two papers accepted at the AutoML Conference 2023 Check out our posters in Berlin!

selected publications

  1. Structure in Deep Reinforcement Learning: A survey and open problems
    Aditya MohanAmy Zhang, and Marius Lindauer
    Journal of Artificial Intelligence Research (JAIR), 2024
  2. AutoRL Hyperparameter Landscapes
    Aditya MohanCarolin Benjamins, Konrad Wienecke, Alexander Dockhorn, and Marius Lindauer
    Proceedings of the Second International Conference on Automated Machine Learning (AutoML), 2023
  3. Contextualize Me - The Case for Context in Reinforcement Learning
    Carolin BenjaminsTheresa EimerF. SchubertAditya Mohan, Andre Biedenkapp, Bodo RosenhahnFrank Hutter, and Marius Lindauer
    Transactions on Machine Learning Research (TMLR), 2023