تقرير
An Adiabatic Theorem for Policy Tracking with TD-learning
| العنوان: | An Adiabatic Theorem for Policy Tracking with TD-learning |
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| المؤلفون: | Walton, Neil |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science Mathematics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Mathematics - Probability |
| الوصف: | We evaluate the ability of temporal difference learning to track the reward function of a policy as it changes over time. Our results apply a new adiabatic theorem that bounds the mixing time of time-inhomogeneous Markov chains. We derive finite-time bounds for tabular temporal difference learning and $Q$-learning when the policy used for training changes in time. To achieve this, we develop bounds for stochastic approximation under asynchronous adiabatic updates. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2010.12848 |
| رقم الأكسشن: | edsarx.2010.12848 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |