13 Reinforcement Learning: Policy Gradients, Q Learning, AlphaGo, AlphaStar (MLVU2019) @riskone1
13 Reinforcement Learning: Policy Gradients, Q Learning, AlphaGo, AlphaStar (MLVU2019) @riskone1
Uploaded March 2019 | Updated September 2026, 6 days ago
slides: mlvu.github.io/lectures/71.Reinforcement%20Learning.annotated.pdf
course materials: mlvu.github.io

Today we discuss the most generic abstract task in machine learning: reinforcement learning. Reinforcement Learning models an agent that interacts with its environment. We dicuss the basic task of RL, and three ways to solve it: random search, policy gradients and Q learning.

Then, we have a look at some recent successes of deep reinforcement learning (combining RL with deep neural networks): AlphaGO, AlphaZero and AlphaStar.
13 Reinforcement Learning: Policy Gradients, Q Learning, AlphaGo, AlphaStar (MLVU2019)2 Linear Models 1: Hyperplanes, Random Search, Gradient Descent (MLVU2019)MLVU 7.4: Making it work10 Tree Models and Ensembles: Decision Trees, AdaBoost, Gradient Boosting (MLVU2020)01 Introduction to Machine Learning (MLVU2018)MLVU 7.3: Convolutions9 Deep Learning 2: Generative models, GANs, Variational Autoencoders (VAEs) (MLVU2019)MLVU 10.3: Ensembling: stacking, bagging and random forestsMLVU 5.1: Introduction to probability09 Deep Learning 2: GANs, Variational Autoencoders (MLVU2018)4 Methodology for pre-processing, PCA, Eigenfaces (MLVU2020)MLVU 3.5: Statistics for Machine Learning Experiments
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13 Reinforcement Learning: Policy Gradients, Q Learning, AlphaGo, AlphaStar (MLVU2019)

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