SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration

Published: 23 Sept 2023, Last Modified: 23 Sept 2023Accepted by TMLREveryoneRevisionsBibTeX
Abstract: The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents. Skill reuse is one of the most common approaches, but current methods have considerable limitations. For example, fine-tuning an existing policy frequently fails, as the policy can degrade rapidly early in training. In a similar vein, distillation of expert behavior can lead to poor results when given sub-optimal experts. We compare several common approaches for skill transfer on multiple domains including changes in task and system dynamics. We identify how existing methods fail and introduce an alternative approach to mitigate these problems. Our approach learns to sequence temporally-extended skills for exploration but learns the final policy directly from the raw experience. This conceptual split enables rapid adaptation and thus efficient data collection but without constraining the final solution. It significantly outperforms many classical methods across a suite of evaluation tasks and we use a broad set of ablations to highlight the importance of different components of our method.
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: Camera ready version.
Assigned Action Editor: ~Marcello_Restelli1
Submission Number: 1268