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COMP 138: Reinforcement Learning Instructor : Jivko Sinapov Webpage : https://www.eecs.tufts.edu/~jsinapov/teaching/comp150_RL_Fall2020/ BE a reinforcement learner You, as a class, will act as the learning agent BE a reinforcement learner

  1. COMP 138: Reinforcement Learning Instructor : Jivko Sinapov Webpage : https://www.eecs.tufts.edu/~jsinapov/teaching/comp150_RL_Fall2020/

  2. BE a reinforcement learner ● You, as a class, will act as the learning agent

  3. BE a reinforcement learner ● You, as a class, will act as the learning agent ● Actions: wave, clap, or nod

  4. BE a reinforcement learner ● You, as a class, will act as the learning agent ● Actions: wave, clap, or nod ● Observations: color, reward

  5. BE a reinforcement learner ● You, as a class, will act as the learning agent ● Actions: wave, clap, or nod ● Observations: color, reward ● Goal: find an optimal policy

  6. BE a reinforcement learner ● You, as a class, will act as the learning agent ● Actions: wave, clap, or stand ● Observations: color, reward ● Goal: find an optimal policy – What is a policy? What makes a policy optimal?

  7. How did you do it? ● What is your policy, and how is it represented? ● What does the world look like?

  8. What actually happened...

  14. What actually happened...

  15. Now, let’s formalize this (board or writing projector)

  16. About this course ● Reinforcement Learning theory & practice ● Theory at the start and practice towards end ● Syllabus = the course web page: https://www.eecs.tufts.edu/~jsinapov/teaching/comp150_RL/

  17. Where does RL fall within the field of Artificial Intelligence?

  18. Where does RL fall within the field of Artificial Intelligence? ● AI → ML → RL

  19. Where does RL fall within the field of Artificial Intelligence? ● AI → ML → RL ● Type of Machine Learning: – Supervised : learn from labeled examples – Unsupervised : learn from unlabeled examples – Reinforcement : learn through interaction

  20. Reduced Formalism

  21. Reduced Formalism (board or writing projector)

  22. Take-home Message ● Agent’s perspective: only the policy is under control ● State representation and reward function are given ● Focus on policy algorithms ● Appeal: program agents by just specifying goals ● Practice: need to pick state representation and reward function

  23. Example Applications

  24. Example Applications

  25. Reading Assignment ● Chapter 1 and 2 of Sutton and Barto ● Reading response on Canvas due 9/11 before class starts

  26. Programming Assignments ● Students are required to complete 4 minor programming assignments of their choosing ● Default options: programing exercises from Sutton and Barto (let’s look at some examples)

  27. Discussion Moderation ● Each student will lead a reading discussion once during the semester ● Students can team up in a pair ● Sign up sheet will be posted to Canvas tonight ● Extra credit for anyone who volunteers for slots in the next week ● Presentation materials / notes or description of what will be discussed should be emailed to me 48 hours before the class

  28. Next time...

  31. COMP 150: Reinforcement Learning

  32. Domains and Applications

  33. Curriculum Learning . . . . . . Example QuickChess game variants

  34. The Curriculum Learning Problem Task = MDP Environment Task Creatjon State Actjon Reward Agent Target task Sequencing Transfer Learning [ Narverkar et al 2016 ]

  35. Textbook The authors have made the book available: http://incompleteideas.net/book/bookdraft2017nov5.pdf

  36. Course Organization ● Taught as a seminar: students take turns presenting the readings ● Will cover both theory and practice ● Final projects – you will complete a project in which you ask (and then answer) a relevant RL research question

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COMP 138: Reinforcement Learning Instructor : Jivko Sinapov Webpage :

COMP 138: Reinforcement Learning Instructor : Jivko Sinapov Webpage :

COMP 138: Reinforcement Learning Instructor : Jivko Sinapov Webpage : https://www.eecs.tufts.edu/~jsinapov/teaching/comp150_RL_Fall2020/ Announcements Homework 1 is due this Friday Need Help? Let Andre or I know Reading Assignment

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COMP 138: Reinforcement Learning Instructor : Jivko Sinapov Webpage :

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