Aditya Mohan
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Welfengarten 1
30167 Hannover
30167 Hannover
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Aditya Mohan
Telefon
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I work on methods to deploy Reinforcement Learning in applications beyond games and simulators. This involves working on Representation Learning in RL, Meta-RL, Optimization in RL, and Algorithm configuration.
Research Interests
- Generalization in Reinforcement Learning
- Representation learning
- Automated Reinforcement Learning
- Meta Reinforcement Learning
Curriculum Vitae
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Work Experience
October, 2021 - Present: Doctoral Researcher in the Automated Machine Learning group at Leibniz University of Hannover
September, 2020 - December, 2020: Research Intern at Learning and Intelligent Systems Group at the Technical University of Berlin
July, 2018 - September, 2019: Analyst in the Risk Consulting team in KPMG India
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Education
October, 2021 - Present: Ph.D. candidate at the Leibniz University Hannover
2019 - 2021: M.Sc. in Autonomous Systems at the Teschnische Universität Berlin and EURECOM
- Thesis: AI agents that quickly adapt to a partner for Ad.hoc cooperation in the game of Hanabi
- Supervisor: Prof. Dr. Klaus Obermayer
2014 - 2018: B.Tech in Electronics and Communication Engineering at Manipal Institute of Technology
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Office Hours
Feel free to book a 30-minute slot in my calendar if you want to want to talk to me. These office hours are open to everyone. For longer meetings or different times, please email me.
Publications
First
1
2025
Jabs D, Mohan A, Lindauer M. Moments Matter: Stabilizing Policy Optimization using Return Distributions. in 2025 Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2025). 2025
Mohan A, Eimer T, Benjamins C, Lindauer M, Biedenkapp A. Mighty: A Comprehensive Tool for studying Generalization, Meta-RL and AutoRL. in 18th European Workshop on Reinforcement Learning (EWRL). 2025 Epub 2025 Sep.
2024
Becktepe J, Dierkes J, Benjamins C, Mohan A, Salinas D, Rajan R et al. ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. in 17th European Workshop on Reinforcement Learning (EWRL 2024). 2024 Epub 2024. doi: 10.48550/arXiv.2409.18827
Benjamins C, Cenikj G, Nikolikj A, Mohan A, Eftimov T, Lindauer M. Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization. in Genetic and Evolutionary Computation Conference (GECCO). Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation (SIGEVO). 2024. S. 563 - 566 doi: 10.1145/3638530
Mohan A, Zhang A, Lindauer M. Structure in Deep Reinforcement Learning: A Survey and Open Problems. Journal of Artificial Intelligence Research. 2024 Apr;79:1167-1236. doi: 10.1613/jair.1.15703
Mohan A, Lindauer M. Towards Enhancing Predictive Representations using Relational Structure in Reinforcement Learning. in The 17th European Workshop on Reinforcement Learning (EWRL 2024). 2024
Tornede A, Deng D, Eimer T, Giovanelli J, Mohan A, Ruhkopf T et al. AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. Transactions on Machine Learning Research. 2024 Feb 9. Epub 2024 Feb 9. doi: 10.48550/arXiv.2306.08107
2023
Benjamins C, Eimer T, Schubert FG, Mohan A, Döhler S, Biedenkapp A et al. Contextualize Me – The Case for Context in Reinforcement Learning. Transactions on Machine Learning Research. 2023 Jun 5. Epub 2023 Jun 5. doi: 10.48550/arXiv.2202.04500
Benjamins C, Eimer T, Schubert FG, Mohan A, Döhler S, Biedenkapp A et al. Extended Abstract: Contextualize Me -- The Case for Context in Reinforcement Learning. in The 16th European Workshop on Reinforcement Learning (EWRL 2023). 2023 Epub 2023.
Loni M, Mohan A, Asadi M, Lindauer M. Learning Activation Functions for Sparse Neural Networks. in Second International Conference on Automated Machine Learning. PMLR. 2023
Mohan A, Zhang A, Lindauer M. A Patterns Framework for Incorporating Structure in Deep Reinforcement Learning. in The 16th European Workshop on Reinforcement Learning (EWRL 2023). 2023
Mohan A, Benjamins C, Wienecke K, Dockhorn A, Lindauer M. AutoRL Hyperparameter Landscapes. in Conference proceeding: Second Internatinal Conference on Automated Machine Learning. PMLR. 2023. (Proceedings of Machine Learning Research). doi: 10.48550/arXiv.2304.02396
Mohan A, Benjamins C, Wienecke K, Dockhorn A, Lindauer M. Extended Abstract: AutoRL Hyperparameter Landscapes. 2023. Abstract von European Workshop on Reinforcement Learning 2023, Brüssel.
Ruhkopf T, Mohan A, Deng D, Tornede A, Hutter F, Lindauer M. MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information. Transactions on Machine Learning Research. 2023 Apr 18. Epub 2023 Apr 18.
2022
Mohan A, Ruhkopf T, Lindauer M. Towards Meta-learned Algorithm Selection using Implicit Fidelity Information. in ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML). 2022
