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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
Use of Game Theory for deep learning or deep learning for Game Theory
Designing optimal mechanisms for environments involving strategic agents using data-driven approaches, to overcome the difficulty of solving it analytically
Demystifying convergence of certain machine learning algorithms using established results from Game theory
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