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| The Option-Critic (OC) framework can extract transferrable abstract knowledge without requiring any environment-specific prior knowledge, learning options (a form of temporal abstract policy) end-to-end. However, the OC framework exhibits lower data efficiency in transfer tasks. During the learning process, each option considers the entire task's state space, thereby increasing the scale of policy space search. This paper proposes an Option Learning algorithm based on Representation Erasure, which introduces the Representation Erasure method to clearly quantify the influence of each dimension on high-level and low-level policy learning. It identifies and erases dimensions that significantly interfere with training, effectively reducing the scale of policy space search. Through theoretical derivation and experimental validation, this paper demonstrates the effectiveness of the Representation Erasure-based Option Learning algorithm. |
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| Keywords:Artificial Intelligence; Transfer Learning; Hierarchical Reinforcement Learning; Representation Erasure |
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