Lifelong Learning with a Changing Action Set

Yash Chandak
Yash Chandak
Georgios Theocharous
Georgios Theocharous
Chris Nota
Chris Nota

national conference on artificial intelligence, 2020.

Cited by: 0|Bibtex|Views11
Other Links: academic.microsoft.com|arxiv.org

Abstract:

In many real-world sequential decision making problems, the number of available actions (decisions) can vary over time. While problems like catastrophic forgetting, changing transition dynamics, changing rewards functions, etc. have been well-studied in the lifelong learning literature, the setting where the action set changes remains u...More

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