Statistical Reinforcement Learning for High-Risk Decision Problems
Watch the Keynote and Panel Discussion
In recent years, reinforcement learning (RL) has received significant attention due to high-profile successes in areas such as Chess, Go, and protein folding. Furthermore, RL has long been a cornerstone in e-commerce where it is used to personalize customer recommendations.
In this Statistically Speaking. Dr. Eric Laber explores the lesser-studied use of RL for high-stakes settings in which data volume is low and the cost of experimentation is high. In various applications such as precision medicine, statistical efficiency and safety are paramount.
What you’ll learn:
- The importance of understanding basic concepts of reinforcement learning.
- Why statistical thinking is critical for application of RL in high-stakes problems.
- How RL can facilitate the personalization of customer recommendations.
- Why exploration of RL is relevant when data volume is low and cost is high