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Stochastic control deals with finding an optimal control signal for adynamical system in a setting with uncertainty, playing a key role in numerousapplications. The linear quadratic Gaussian (LQG) is a widely-used setting,where the system dynamics is represented as a linear Gaussian statespace (SS)model, and the objective function is quadratic. For this setting, the optimalcontroller is obtained in closed form by the separation principle. However, inpractice, the underlying system dynamics often cannot be faithfully captured bya fully known linear Gaussian SS model, limiting its performance. Here, wepresent LQGNet, a stochastic controller that leverages data to operate underpartially known dynamics. LQGNet augments the state tracking module ofseparation-based control with a dedicated trainable algorithm. The resultingsystem preserves the operation of classic LQG control while learning to copewith partially known SS models without having to fully identify the dynamics.We empirically show that LQGNet outperforms classic stochastic control byovercoming mismatched SS models.

LQGNet: Hybrid Model-Based and Data-Driven Linear Quadratic Stochastic Control