Aditya Mohan
Address
Welfengarten 1
30167 Hannover
Building
Room
Aditya Mohan
Address
Welfengarten 1
30167 Hannover
Building
Room

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

  • 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

     

  • 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

  • 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

Showing results 1 - 13 out of 13

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.
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 Epub 2024.
Mohan A, Zhang A, Lindauer M. Structure in Deep Reinforcement Learning: A Survey and Open Problems. Journal of Artificial Intelligence Research. 2024 Apr. Epub 2024 Apr.
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;2023(6). 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 Second International Conference on Automated Machine Learning. PMLR. 2023 Epub 2023 Jul 20. doi: 10.48550/arXiv.2304.02396
Mohan A, Benjamins C, Wienecke K, Dockhorn A, Lindauer M. Extended Abstract: AutoRL Hyperparameter Landscapes. In The 16th European Workshop on Reinforcement Learning (EWRL 2023). 2023
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 Epub 2022 Jun 7.