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This paper presents machine learning techniques and deep reinforcementlearningbased algorithms for the efficient resolution of nonlinear partialdifferential equations and dynamic optimization problems arising in investmentdecisions and derivative pricing in financial engineering. We survey recentresults in the literature, present new developments, notably in the fullynonlinear case, and compare the different schemes illustrated by numericaltests on various financial applications. We conclude by highlighting somefuture research directions.

Neural networks-based algorithms for stochastic control and PDEs in finance