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Actor-Critic learning for mean-field control in continuous time
We study policy gradient for mean-field control in continuous time in a reinforcement learning setting.
Neural networks-based algorithms for stochastic control and PDEs in finance
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.
Optimization and Control Computational Finance
Neural networks-based backward scheme for fully nonlinear PDEs
We propose a numerical method for solving high dimensional fully nonlinear partial differential equations (PDEs).
