Overview of Deep Reinforcement Learning Methods - presented by Prof. Steven L. Brunton

Overview of Deep Reinforcement Learning Methods

Prof. Steven L. Brunton

Prof. Steven L. Brunton
Slide at 00:44
REINFORCEMENT LEARNING Model-based RL DATA-DRIVEN SCIENCE AND Markoo Decision Process P.s..s.a) ENGINEERING Policy Iteration we(s,a) Actor Machine Learning, Critic Dynamical Systems, Value Iteration V(s) and Control Dynamic programming Steven L. Brunton . J. Nathan Kutz & Bellman optimality Nonlinear Dynamics Deep -x f(x(t),u(f).t)dt Optimal Control & HIB NEW CHAPTER IN 2ND EDITION HTTPS://FACULTY.WASHINGTON.EDU/~SBRUNTON/DATABOOKRL.P!
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References
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    https://faculty.washington.edu/sbrunton/databookRL.pdf
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Summary (AI generated)

I hope you are enjoying this as much as I am. Today, I am going to touch on the different techniques of deep Reinforcement learning. However, due to the vastness of the field, I will only be discussing the tip of the iceberg. I have previously shared a video where I talked about the use of deep neural networks and reinforcement learning at a high level. My focus then was on providing examples and demonstrations of the cool things you can do with these techniques. In this lecture, I will delve deeper into the algorithms and explore how the different flavors of deep Reinforcement learning fit into the classic pictures we have been developing in our previous lectures.

Without further ado, I will jump right in and discuss some exciting concepts.