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Decision-Making and Learning in an Unknown Environment

In: Reinforcement Learning From Scratch

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  • Uwe Lorenz

Abstract

This chapter describes how the agent can explore an unknown environmental system in which it has been placed. In doing so, he discovers states with rewards and has to optimize the paths to these goals, on the one hand, but also explore new goals, on the other hand. In doing so, he must consider a trade-off between exploitation and exploration. On the one hand, he has to collect the possible reward of already discovered goals; on the other, hand he has to manage the exploration of better paths or the discovery of new goals. There are different approaches to this; some aim at processing experiences made in such a way that the agent behaves better under the same conditions in the future “model-free methods”; and others that aim at optimizing models that can predict what would happen if certain actions are chosen.

Suggested Citation

  • Uwe Lorenz, 2022. "Decision-Making and Learning in an Unknown Environment," Springer Books, in: Reinforcement Learning From Scratch, chapter 4, pages 47-121, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-09030-1_4
    DOI: 10.1007/978-3-031-09030-1_4
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