تقرير
Weight Clipping for Deep Continual and Reinforcement Learning
| العنوان: | Weight Clipping for Deep Continual and Reinforcement Learning |
|---|---|
| المؤلفون: | Elsayed, Mohamed, Lan, Qingfeng, Lyle, Clare, Mahmood, A. Rupam |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence |
| الوصف: | Many failures in deep continual and reinforcement learning are associated with increasing magnitudes of the weights, making them hard to change and potentially causing overfitting. While many methods address these learning failures, they often change the optimizer or the architecture, a complexity that hinders widespread adoption in various systems. In this paper, we focus on learning failures that are associated with increasing weight norm and we propose a simple technique that can be easily added on top of existing learning systems: clipping neural network weights to limit them to a specific range. We study the effectiveness of weight clipping in a series of supervised and reinforcement learning experiments. Our empirical results highlight the benefits of weight clipping for generalization, addressing loss of plasticity and policy collapse, and facilitating learning with a large replay ratio. Comment: Published in the First Reinforcement Learning Conference (RLC 2024). Code is available at https://github.com/mohmdelsayed/weight-clipping |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.01704 |
| رقم الأكسشن: | edsarx.2407.01704 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |