Aditya Mohan

Sequential decision-making problems in the real world often come with abundant information that is typically underutilized in Reinforcement Learning (RL). My research focuses on leveraging this information to enhance RL techniques. Specifically, I aim to explore how RL agents can utilize task structures to improve sample efficiency and generalization. In doing so, my work intersects various RL domains, including Contextual RL, Automated RL, Meta-RL, and state abstractions.

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. Additionally, I have worked on problems in Robotics, Reinforcement Learning, and Meta-Learning in both single-agent and multi-agent settings.

Beyond my research, I have a passion for singing, playing piano and guitar, composing music, dancing, and cooking. If you’d like to chat about research, music, food, or anything else, don’t hesitate to book a slot in my calender