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Artificial Neural Networks as Estimators for State Values and the Action Selection

In: Reinforcement Learning From Scratch

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  • Uwe Lorenz

Abstract

Usually, the available resources are not sufficient to tabulate policy, valuation function, or model. Therefore, this chapter introduces parameterized estimators that allow us, for example, to estimate the valuation of states even if they have not been observed in exactly the same form before. In particular, the so-called artificial neural networks are discussed. We will also learn possibilities to use such estimators to create parameterized policies which, for a given state, can produce and improve a useful probability distribution over the available actions.

Suggested Citation

  • Uwe Lorenz, 2022. "Artificial Neural Networks as Estimators for State Values and the Action Selection," Springer Books, in: Reinforcement Learning From Scratch, chapter 5, pages 123-173, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-09030-1_5
    DOI: 10.1007/978-3-031-09030-1_5
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