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Explore Policy Gradient Methods | Reinforcement Learning Part 6 with 77,919 views, 2,375 likes, and 118 comments. Experience the impact of this video content that has captured audience attention.

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 Policy Gradient Methods are among the most effective techniques in Reinforcement Learning. In this video, we'll motivate their design, observe their behavior and understand their background theory. 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 FOR THE FULL SERIES [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://youtu.be/TCCjZe0y4Qc [3] J. Achiam. Spinning Up in Deep Reinforcement Learning, OpenAI, 2018 ADDITIONAL SOURCES FOR THIS VIDEO [4] J. Achiam, Spinning Up in Deep Reinforcement Learning: Intro to Policy Optimization, OpenAI, 2018, https://spinningup.openai.com/en/latest/spinningup/rl_intro3.html [5] D. Silver, Lecture 7: Policy Gradient Methods, Deepmind, 2015, https://youtu.be/KHZVXao4qXs TIMESTAMPS 0:00 Introduction 0:50 Basic Idea of Policy Gradient Methods 2:30 A Familiar Shape 4:23 Motivating the Update Rule 10:51 Fixing the Update Rule 12:55 Example: Windy Highway 16:47 A Problem with Naive PGMs 19:43 Reinforce with Baseline 21:42 The Policy Gradient Theorem 25:20 General Comments 28:02 Thanking The Sources LINKS Windy Highway: https://github.com/Duane321/mutual_information/tree/main/videos/policy_gradient_methods NOTES [1] When motivating the update rule with an animation protopoints and theta bars, I don't specify alpha. That's because the lengths of the gradient arrows can only be interpretted on a relative basis. Their absolute numeric values can't be deduced from the animation because there was some unmentioned scaling done to make the animation look natural. Mentioning alpha would have make this calculation possible to attempt, so I avoided it.

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