تقرير
Reward Tweaking: Maximizing the Total Reward While Planning for Short Horizons
العنوان: | Reward Tweaking: Maximizing the Total Reward While Planning for Short Horizons |
---|---|
المؤلفون: | Tessler, Chen, Mannor, Shie |
سنة النشر: | 2020 |
المجموعة: | Computer Science Statistics |
مصطلحات موضوعية: | Computer Science - Machine Learning, Statistics - Machine Learning |
الوصف: | In reinforcement learning, the discount factor $\gamma$ controls the agent's effective planning horizon. Traditionally, this parameter was considered part of the MDP; however, as deep reinforcement learning algorithms tend to become unstable when the effective planning horizon is long, recent works refer to $\gamma$ as a hyper-parameter -- thus changing the underlying MDP and potentially leading the agent towards sub-optimal behavior on the original task. In this work, we introduce \emph{reward tweaking}. Reward tweaking learns a surrogate reward function $\tilde r$ for the discounted setting that induces optimal behavior on the original finite-horizon total reward task. Theoretically, we show that there exists a surrogate reward that leads to optimality in the original task and discuss the robustness of our approach. Additionally, we perform experiments in high-dimensional continuous control tasks and show that reward tweaking guides the agent towards better long-horizon returns although it plans for short horizons. Comment: Preprint |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2002.03327 |
رقم الأكسشن: | edsarx.2002.03327 |
قاعدة البيانات: | arXiv |
الوصف غير متاح. |