Breaking through Data Scarcity: Knowledge Transfer in Offline Reinforcement Learning
Keywords: Reinforcement Learning
Abstract: We focus on knowledge transfer in offline reinforcement learning (RL), which aims to significantly improve the learning of an optimal policy in a target task based on a pre-collected dataset without further interactions with the environment. Data scarcity and high-dimensional feature spaces seriously pose challenges to offline RL in many real-world applications, and knowledge transfer offers a promising solution. We propose a novel and comprehensive knowledge transfer framework for offline RL, which carefully considers the relationship between the target and source tasks within the linear Markov decision process (MDP) framework. This enables efficient knowledge transfer from related source tasks to enhance learning in the target task and effectively address data scarcity concerns in offline RL. Our main contributions include establishing a relationship with the learning process between the target task and source task, introducing an effective and robust knowledge transfer technique to reduce the suboptimality of the learned policy, and demonstrating the significant effectiveness of the knowledge transfer framework through detailed theoretical analysis. Our work significantly contributes to the advancement of offline RL by providing a practical and robust framework for knowledge transfer facilitating more efficient and effective data utilization in various applications.
Primary Area: reinforcement learning
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Submission Number: 2309
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