Report
Continual State Representation Learning for Reinforcement Learning using Generative Replay
| العنوان: | Continual State Representation Learning for Reinforcement Learning using Generative Replay |
|---|---|
| المؤلفون: | Caselles-Dupré, Hugo, Garcia-Ortiz, Michael, Filliat, David |
| سنة النشر: | 2018 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | We consider the problem of building a state representation model in a continual fashion. As the environment changes, the aim is to efficiently compress the sensory state's information without losing past knowledge. The learned features are then fed to a Reinforcement Learning algorithm to learn a policy. We propose to use Variational Auto-Encoders for state representation, and Generative Replay, i.e. the use of generated samples, to maintain past knowledge. We also provide a general and statistically sound method for automatic environment change detection. Our method provides efficient state representation as well as forward transfer, and avoids catastrophic forgetting. The resulting model is capable of incrementally learning information without using past data and with a bounded system size. Comment: Accepted contribution to the Workshop on Continual Learning, NeurIPS 2018 (Neural Information Processing Systems) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1810.03880 |
| رقم الانضمام: | edsarx.1810.03880 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |