Accelerating Task Generalisation with Multi-Level Skill Hierarchies
Thomas P Cannon Özgür Şimşek
Developing reinforcement learning agents that can generalise effectively to new tasks is one of the main challenges in AI research. This paper introduces Fracture Cluster Options (FraCOs), a multi-level hierarchical reinforcement learning method designed to improve generalisation performance. FraCOs identifies patterns in agent behaviour and forms temporally-extended actions (options) based on the expected future usefulness of those patterns, enabling rapid adaptation to new tasks. In tabular settings, FraCOs demonstrates effective transfer and improves performance as the depth of the hierarchy increases. In several complex procedurally-generated environments, FraCOs consistently outperforms state-of-the-art deep reinforcement learning algorithms, achieving superior results in both in-distribution and out-of-distribution scenarios.
Reinforcement Learning, Generalisation, Hierarchical Reinforcement Learning
This work presents Fracture Cluster Options (FraCOs), a novel multi-level hierarchical reinforcement learning framework that improves task generalisation.