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Stanford CS234: Reinforcement Learning - Winter 2019

Stanford University via YouTube

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Details

Go to class

Provider

YouTube
Pricing

Free Online Course
Languages

English
Effort

19 hours
Sessions

Self-Paced
Level

Beginner

Found in

This course introduces reinforcement learning through core challenges and methods such as exploration, generalization, model-free and model-based approaches, deep reinforcement learning, imitation learning, and policy gradients. Learners engage with lectures, coding and written assignments, and an open-ended project.

Syllabus

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 1 - Introduction - Emma Brunskill.
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 2 - Given a Model of the World.
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 3 - Model-Free Policy Evaluation.
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 4 - Model Free Control.
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 5 - Value Function Approximation.
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 6 - CNNs and Deep Q Learning.
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 7 - Imitation Learning.
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 8 - Policy Gradient I.
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 9 - Policy Gradient II.
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 10 - Policy Gradient III & Review.
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 11 - Fast Reinforcement Learning.
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 12 - Fast Reinforcement Learning II.
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 13 - Fast Reinforcement Learning III.
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 15 - Batch Reinforcement Learning.
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 16 - Monte Carlo Tree Search.