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1
Introduction
2
Background
3
Legacy of Decision Making
4
Reinforcement Learning
5
Challenges in AI
6
Huge change in the field
7
Robotics
8
Society
9
Healthcare
10
Application Areas
11
AI Planning
12
Machine Learning
13
Imitation Learning
14
Challenges
15
Rough Plan
16
Markov Decision Processes
17
Decision Policies
18
Horizon
19
Reward Function
Description:
Explore the fundamentals of Reinforcement Learning in this comprehensive lecture by Emma Brunskill from Stanford University. Delve into the legacy of decision-making in AI and its impact on various fields such as robotics, society, and healthcare. Examine key concepts including Markov Decision Processes, decision policies, and reward functions. Gain insights into the challenges faced in AI planning, machine learning, and imitation learning. Understand the significant changes occurring in the field and their implications for future applications.

Reinforcement Learning - Emma Brunskill - Stanford University

Paul G. Allen School
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