CS234 - Reinforcement Learning

CS234: Reinforcement Learning. Instructor: Prof. Emma Brunskill, Department of Computer Science, Stanford University. To realize the dreams and impact of AI requires autonomous systems that learn to make good decisions. Reinforcement learning is one powerful paradigm for doing so, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. This class will provide a solid introduction to the field of reinforcement learning and students will learn about the core challenges and approaches, including generalization and exploration. Through a combination of lectures, and written and coding assignments, students will become well versed in key ideas and techniques for RL. Assignments will include the basics of reinforcement learning as well as deep reinforcement learning — an extremely promising new area that combines deep learning techniques with reinforcement learning. In addition, students will advance their understanding and the field of RL through a final project. You can find more information about this course, such as lecture slides and syllabus, here. (from Stanfordonline)

Lecture 04 - Model Free Control

Go to the Course Home or watch other lectures:

Lecture 01 - Introduction
Lecture 02 - Given a Model of the World
Lecture 03 - Model-Free Policy Evaluation
Lecture 04 - Model Free Control
Lecture 05 - Value Function Approximation
Lecture 06 - CNNs and Deep Q Learning
Lecture 07 - Imitation Learning
Lecture 08 - Policy Gradient I
Lecture 09 - Policy Gradient II
Lecture 10 - Policy Gradient III and Review
Lecture 11 - Fast Reinforcement Learning
Lecture 12 - Fast Reinforcement Learning II
Lecture 13 - Fast Reinforcement Learning III
Lecture 14
Lecture 15 - Batch Reinforcement Learning
Lecture 16 - Monte Carlo Tree Search