Partially Observed Stochastic Optimal Control

Stochastic Optimal Control with Applications to Renewable Energy and Partially Observed Systems

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This thesis develops continuous-time dynamic programming frameworks and shows how dynamic programming and HJB methods can be extended to settings that do not satisfy the classical Markovian and full-observation assumptions through appropriate relaxation and state-reformulation techniques.

A Pontryagin Maximum Principle on the Belief Space for Continuous-Time Stochastic Optimal Control with Discrete Observations

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Abstract This talk presents joint work with Christian Bayer, Saifeddine Ben Naamia, Erik von Schwerin, and Raúl Tempone on continuous-time stochastic optimal control under partial observations available only at discrete time instants. We formulate the problem on the space of beliefs, using the controller’s posterior distribution as the state variable, and derive a Pontryagin maximum principle that accounts for continuous evolution between observations and Bayesian jump updates at observation times. A key result links the adjoint process to the gradient of the value functional on the belief

Eliza Rezvanova

Eliza Rezvanova research focuses on developing novel mathematical modeling and numerical frameworks for the continuous-time stochastic optimal control of large-scale coupled power systems.