Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey

TL;DR

This paper presents a comprehensive framework for curriculum learning in reinforcement learning, categorizing existing methods, analyzing their assumptions and goals, and identifying open problems to guide future research in the field.

Contribution

It introduces a unifying framework for RL curriculum learning, providing a systematic survey and classification of existing approaches and highlighting future research directions.

Findings

Classified RL curriculum learning methods based on assumptions and goals

Identified open problems and challenges in RL curriculum design

Provided a structured framework to guide future RL curriculum research

Abstract

Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. Despite many advances over the past three decades, learning in many domains still requires a large amount of interaction with the environment, which can be prohibitively expensive in realistic scenarios. To address this problem, transfer learning has been applied to reinforcement learning such that experience gained in one task can be leveraged when starting to learn the next, harder task. More recently, several lines of research have explored how tasks, or data samples themselves, can be sequenced into a curriculum for the purpose of learning a problem that may otherwise be too difficult to learn from scratch. In this article, we present a framework for curriculum learning (CL) in reinforcement learning, and use it to survey and classify existing CL methods in terms of their assumptions, capabilities, and goals. Finally, we use our framework to find open problems and suggest directions for future RL curriculum learning research.