  This paper presents machine learning techniques and deep reinforcement
learningbased algorithms for the efficient resolution of nonlinear partial
differential equations and dynamic optimization problems arising in investment
decisions and derivative pricing in financial engineering. We survey recent
results in the literature, present new developments, notably in the fully
nonlinear case, and compare the different schemes illustrated by numerical
tests on various financial applications. We conclude by highlighting some
future research directions.
