RL

Understanding Behavioral Metric Learning

A key approach to state abstraction is approximating behavioral metrics (notably, bisimulation metrics) in the observation space and embedding these learned distances in the representation space. While promising for robustness to task-irrelevant …

Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning

Developing agents capable of exploring, planning and learning in complex open-ended environments is a grand challenge in artificial intelligence (AI). Hierarchical reinforcement learning (HRL) offers a promising solution to this challenge by …

RLMob - Deep Reinforcement Learning for Successive Mobility Prediction

Human mobility prediction is an important task in the field of spatiotemporal sequential data mining and urban computing. Despite the extensive work on mining human mobility behavior, little attention was paid to the problem of successive mobility …

Match Plan Generation in Web Search with Parameterized Action Reinforcement Learning

To achieve good result quality and short query response time, search engines use specific match plans on Inverted Index to help retrieve a small set of relevant documents from billions of web pages. A match plan is composed of a sequence of match …