Deep Reinforcement Learning of Marked Temporal Point Processes
Explore deep reinforcement learning techniques for marked temporal point processes in this 36-minute conference talk. Delve into the modeling of discrete events in continuous time, examining examples like information propagation and knowledge creation. Learn how to represent the timing and marks of events in marked temporal point processes (MTPPs) and understand their applications in scenarios such as optimizing when-to-post strategies and spaced repetition learning. Discover the reinforcement learning setup for continuous time processes, including state representation and policy optimization using policy gradient methods. Gain insights into solving real-world problems like smart repetition scheduling and strategic social media posting through the application of deep reinforcement learning to MTPPs.