Function Approximation | Reinforcement Learning Part 5

The machine learning consultancy: https://truetheta.io Join my email list to get educational and useful articles (and nothing else!): https://mailchi.mp/truetheta/true-theta-email-list Want to work together? See here: https://truetheta.io/about/#want-to-work-together Here, we learn about Function Approximation. This is a broad class of methods for learning within state spaces that are far too large for our previous methods to work. This is part five of a six part series on Reinforcement Learning. SOCIAL MEDIA LinkedIn : https://www.linkedin.com/in/dj-rich-90b91753/ Twitter : https://twitter.com/DuaneJRich Github: https://github.com/Duane321 Enjoy learning this way? Want me to make more videos? Consider supporting me on Patreon: https://www.patreon.com/MutualInformation SOURCES [1] R. Sutton and A. Barto. Reinforcement learning: An Introduction (2nd Ed). MIT Press, 2018. [2] H. Hasselt, et al. RL Lecture Series, Deepmind and UCL, 2021, https://www.youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm SOURCE NOTES This video covers topics from chapters 9, 10 and 11 from [1], with only a light covering of chapter 11. [2] includes a lecture on Function Approximation, which was a helpful secondary source. TIMESTAMP 0:00 Intro 0:25 Large State Spaces and Generalization 1:55 On Policy Evaluation 4:31 How do we select w? 6:46 How do we choose our target U? 9:27 A Linear Value Function 10:34 1000-State Random Walk 12:51 On Policy Control with FA 14:26 The Mountain Car Task 19:30 Off-Policy Methods with FA LINKS 1000-State Random Walk Problem: https://github.com/Duane321/mutual_information/blob/main/videos/function_approximation/large_random_walk.ipynb Mountain Car Task: https://github.com/Duane321/mutual_information/blob/main/videos/function_approximation/mountain_car.ipynb NOTES [1] In the Mountain Car Task, I left out a hyperparameter to tune: Lambda. This controls how far away the evenly spaced proto-points are from any given evaluation point. If lambda is very high, the prototypical points are considered very close together, and they won't do a good job discriminating different values over the state space. But if lambda is too low, then the prototypical points won't share any information beyond a tiny region surrounding each point.