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Reinforcement Learning: Discrete time
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Classroom Contents
Deep Reinforcement Learning of Marked Temporal Point Processes
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- 1 Start
- 2 Deep Reinforcement Learning of Marked Temporal Point Processes
- 3 Many discrete events in continuous time
- 4 Variety of processes behind these events
- 5 Example I: Information propagation
- 6 Example II: Knowledge creation
- 7 Aren't these event traces just time series?
- 8 What are marked temporal point processes?
- 9 What can MTPPs model?
- 10 What can MTPPs model: when-to-post
- 11 What can MTPPs model: spaced-repetition
- 12 How to optimize Agent's policy?
- 13 Optimizing Agent's policy using RL
- 14 Outline
- 15 Representing Marks and Times of MTPPs
- 16 How to represent MTPPs: timing of events
- 17 How to represent MTPPs: marks of events
- 18 How to represent MTPPs: summary
- 19 Reinforcement Learning: Setup
- 20 Reinforcement Learning: Discrete time
- 21 Reinforcement Learning: Continuous time
- 22 RL with entire history as state
- 23 RL state: embedding marks
- 24 RL state: embedding source of event
- 25 RL state in parametrization of the policy
- 26 RL with Asynchronous Feedback
- 27 RL problem with MTPPs: summary
- 28 Policy optimization problem
- 29 Existing approaches have limitations
- 30 Policy Gradient method can be used!
- 31 Policy Gradient: Example iteration
- 32 Spaced repetition: Problem setup
- 33 Spaced repetition to smart repetition
- 34 When-to-post: Problem setup
- 35 When to post with unknown priorities
- 36 When to post with baselines
- 37 Deep Reinforcement Learning for Marked Temporal Point Processes
- 38 Thank you!!
