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Representation Convergence: Mutual Distillation is Secretly a Form of Regularization
Created by
Haebom
Author
Zhengpeng Xie, Jiahang Cao, Changwei Wang, Fan Yang, Marco Hutter, Qiang Zhang, Jianxiong Zhang, Renjing Xu
Outline
This paper argues that mutual distillation between reinforcement learning policies acts as an implicit regularization mechanism that prevents overfitting to irrelevant features. We theoretically demonstrate for the first time that improved policy robustness to irrelevant features leads to improved generalization performance. Experimentally, we demonstrate that mutual distillation between policies contributes to this robustness, enabling the spontaneous emergence of invariant representations for pixel inputs. Rather than aiming for state-of-the-art performance, our goal is to elucidate the fundamental principles of generalization and deepen our understanding of its mechanisms.
Takeaways, Limitations
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Takeaways:
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A Novel Approach to Overfitting in Reinforcement Learning (Reciprocal Distillation Technique)
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Demonstrating the theoretical link between robustness to irrelevant features and generalization performance.
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Observe the spontaneous emergence of invariant representations from pixel inputs and elucidate their mechanisms.
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Limitations:
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Failed to achieve cutting-edge performance
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Further research is needed to determine the generalizability of the presented theory and experimental results.
