Abstract
This entry provides a short introduction to a class of reinforcement learning algorithms, in particular value function approximation, applied to stochastic optimal control problems. The entry demonstrates how core ideas from dynamic programming and Bellman equations are utilized in common data-driven reinforcement learning algorithms, as well as discuss fundamental challenges of the approach.
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Gatsis, K., Pappas, G.J. (2020). Reinforcement Learning for Control Using Value Function Approximation. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_100067-1
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DOI: https://doi.org/10.1007/978-1-4471-5102-9_100067-1
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