My work is focused on finding theoretically grounded methods for problems in single and multi-agent reinforcement learning (RL) which perform well on large, challenging domains.
My research has been generously supported by J.P. Morgan A.I. Fellowship, Google-DeepMind Scholarship and Draper’s Scholarship. Previously, I have also spent time at DeepMind, J.P. Morgan A.I. and Nvidia.
For more information please visit https://anuj-mahajan.github.io/