Accelerating Task Generalisation with Multi-Level Hierarchical Options
Keywords: Reinforcement Learning, Generalisation, Hierarchical Reinforcement Learning
TL;DR: This work presents Fracture Cluster Options (FraCOs), a novel multi-level hierarchical reinforcement learning framework that improves task generalisation.
Abstract: Creating reinforcement learning agents that generalise effectively to new tasks is a key challenge in AI research. This paper introduces Fracture Cluster Options (FraCOs), a multi-level hierarchical reinforcement learning method that achieves state-of-the-art performance on difficult generalisation tasks. FraCOs identifies patterns in agent behaviour and forms 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 it grows in hierarchical depth. We evaluate FraCOs against state-of-the-art deep reinforcement learning algorithms in several complex procedurally generated environments. Our results show that FraCOs achieves higher in-distribution and out-of-distribution performance than competitors.
Primary Area: reinforcement learning
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Submission Number: 12221
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