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Stanford CS234: Reinforcement Learning

Stanford,, Winter 2019 , Prof. Emma Brunskill

Updated On 02 Feb, 19

Overview

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.

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Lecture 15: Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 16 - Monte Carlo Tree Search

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Lecture Details

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

Professor Emma Brunskill, Stanford University
http://onlinehub.stanford.edu/

Professor Emma Brunskill
Assistant Professor, Computer Science
Stanford AI for Human Impact Lab
Stanford Artificial Intelligence Lab
Statistical Machine Learning Group

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs234/index.html

0:00 Introduction
0:58 Class Structure
1:26 Monte Carlo Tree Search
3:42 Model-Based Reinforcement Learning
4:19 Model-Based and Model-Free RL
7:04 Advantages of Model-Based RL
10:57 MDP Model Refresher
13:11 Table Lookup Model
20:00 Sample-Based Planning
21:11 Back to the AB Example
38:46 Simple Monte-Carlo Search
42:20 Monte-Carlo Tree Search (MCTS)
48:48 Upper Confidence Tree (UCT) Search
52:43 Case Study the Game of Go
54:46 Applying Monte-Carlo Tree Search (1)
56:14 Applying Monte-Carlo Tree Search (5)