Deep Reinforcement Learning of Marked Temporal Point Processes
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Overview
Syllabus
Start
Deep Reinforcement Learning of Marked Temporal Point Processes
Many discrete events in continuous time
Variety of processes behind these events
Example I: Information propagation
Example II: Knowledge creation
Aren't these event traces just time series?
What are marked temporal point processes?
What can MTPPs model?
What can MTPPs model: when-to-post
What can MTPPs model: spaced-repetition
How to optimize Agent's policy?
Optimizing Agent's policy using RL
Outline
Representing Marks and Times of MTPPs
How to represent MTPPs: timing of events
How to represent MTPPs: marks of events
How to represent MTPPs: summary
Reinforcement Learning: Setup
Reinforcement Learning: Discrete time
Reinforcement Learning: Continuous time
RL with entire history as state
RL state: embedding marks
RL state: embedding source of event
RL state in parametrization of the policy
RL with Asynchronous Feedback
RL problem with MTPPs: summary
Policy optimization problem
Existing approaches have limitations
Policy Gradient method can be used!
Policy Gradient: Example iteration
Spaced repetition: Problem setup
Spaced repetition to smart repetition
When-to-post: Problem setup
When to post with unknown priorities
When to post with baselines
Deep Reinforcement Learning for Marked Temporal Point Processes
Thank you!!
Taught by
International Centre for Theoretical Sciences