تقرير
Momentum in Reinforcement Learning
| العنوان: | Momentum in Reinforcement Learning |
|---|---|
| المؤلفون: | Vieillard, Nino, Scherrer, Bruno, Pietquin, Olivier, Geist, Matthieu |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | We adapt the optimization's concept of momentum to reinforcement learning. Seeing the state-action value functions as an analog to the gradients in optimization, we interpret momentum as an average of consecutive $q$-functions. We derive Momentum Value Iteration (MoVI), a variation of Value Iteration that incorporates this momentum idea. Our analysis shows that this allows MoVI to average errors over successive iterations. We show that the proposed approach can be readily extended to deep learning. Specifically, we propose a simple improvement on DQN based on MoVI, and experiment it on Atari games. Comment: AISTATS 2020 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1910.09322 |
| رقم الأكسشن: | edsarx.1910.09322 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |