Haikuan Huang

Stabilizing Reinforcement Learning in Dynamic Environment with Application to Online Recommendation

2018

Deep reinforcement learning has shown great potential in improving system performance autonomously, by learning from iterations with the environment. However, traditional reinforcement learning approaches are designed to work in static environments. In many real-world problems, …