Modern
problems involve strategic agents, private information, unknown information, and
opportunities to explore and interact with agents, etc.
ML
and Game Theory are well investigated as individual problems. Interesting
research questions arise when we try to meld them together
Currently we are looking at two aspects:
Use of MAB and Q-Learning with Game
Theory
Designing optimal mechanisms forenvironments involving strategic agents using data-driven approaches, to
overcome the difficulty of solving it analytically
Use of Game Theory for deep learning or
deep learning for Game Theory
Demystifying convergence of certain machine learning algorithms using
established results from Game theory
