Lifelong Learning with a Changing Action Set

  • Yash Chandak University of Massachusetts Amherst
  • Georgios Theocharous Adobe Research
  • Chris Nota University of Massachusetts Amherst
  • Philip Thomas University of Massachusetts Amherst

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 size of the action set changes remains unaddressed. In this paper, we present first steps towards developing an algorithm that autonomously adapts to an action set whose size changes over time. To tackle this open problem, we break it into two problems that can be solved iteratively: inferring the underlying, unknown, structure in the space of actions and optimizing a policy that leverages this structure. We demonstrate the efficiency of this approach on large-scale real-world lifelong learning problems.

Published
2020-04-03
Section
AAAI Technical Track: Machine Learning