# Lifelong Learning with a Changing Action Set

## TL;DR

This paper introduces an algorithm for lifelong learning in sequential decision making where the set of available actions changes over time, addressing a previously unstudied problem.

## Contribution

It proposes a novel method that infers action structure and adapts policies dynamically to changing action sets in lifelong learning scenarios.

## Key findings

- Efficiently handles large-scale real-world problems
- Successfully infers underlying action structure
- Adapts policies to changing action sets

## 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 unaddressed. In this paper, we present 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.