Robust Knowledge Transfer in Tiered Reinforcement Learning

• URL: http://arxiv.org/abs/2302.05534v3
• Date: Thu, 13 Jun 2024 14:54:41 GMT
• Title: Robust Knowledge Transfer in Tiered Reinforcement Learning
• Authors: Jiawei Huang, Niao He,
• Abstract summary: We study the Tiered Reinforcement Learning setting, where the goal is to transfer knowledge from the low-tier (source) task to the high-tier (target) task. Unlike previous work, we do not assume the low-tier and high-tier tasks share the same dynamics or reward functions. We propose novel online learning algorithms such that, for the high-tier task, it can achieve constant regret on partial states depending on the task similarity.
• Score: 22.303882476904295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study the Tiered Reinforcement Learning setting, a parallel transfer learning framework, where the goal is to transfer knowledge from the low-tier (source) task to the high-tier (target) task to reduce the exploration risk of the latter while solving the two tasks in parallel. Unlike previous work, we do not assume the low-tier and high-tier tasks share the same dynamics or reward functions, and focus on robust knowledge transfer without prior knowledge on the task similarity. We identify a natural and necessary condition called the ``Optimal Value Dominance'' for our objective. Under this condition, we propose novel online learning algorithms such that, for the high-tier task, it can achieve constant regret on partial states depending on the task similarity and retain near-optimal regret when the two tasks are dissimilar, while for the low-tier task, it can keep near-optimal without making sacrifice. Moreover, we further study the setting with multiple low-tier tasks, and propose a novel transfer source selection mechanism, which can ensemble the information from all low-tier tasks and allow provable benefits on a much larger state-action space.

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