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Found 4 papers, 4 papers with code
Extended Kalman filter -- Koopman operator for tractable stochastic optimal control
It has been more than seven decades since the introduction of the theory of dual control \cite{feldbaum1960dual}.
Actively Learning Reinforcement Learning: A Stochastic Optimal Control Approach
In this paper we propose a framework towards achieving two intertwined objectives: (i) equipping reinforcement learning with active exploration and deliberate information gathering, such that it regulates state and parameter uncertainties resulting from modeling mismatches and noisy sensory; and (ii) overcoming the huge computational cost of stochastic optimal control.
A Control Approach for Nonlinear Stochastic State Uncertain Systems with Probabilistic Safety Guarantees
We propose a control design approach that first generates a control policy for nonlinear deterministic models with full state observation.
Monte Carlo Grid Dynamic Programming: Almost Sure Convergence and Probability Constraints
This paper presents a Monte Carlo-based sampling approach for the state space and an interpolation procedure for the resulting value function, dependent on the process noise density, in a "self-approximating" fashion, eliminating the need for ordering or set-membership tests.
