Lifelong Learning with a Changing Action Set

Understand

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.