Reinforcement Learning Foundations

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01 Introduction

001 Reinforcement learning in a nutshell.mp4

02:00

02 Getting Started with Reinforcement Learning

001 Terms in reinforcement learning.mp4

02:18

002 A basic RL problem.mp4

04:26

003 Markov decision process.mp4

04:24

004 A basic RL solution.mp4

02:08

03 Reinforcement Learning Algorithms

001 Monte Carlo method.mp4

03:01

002 Temporal difference methods.mp4

01:09

003 Other RL algorithms.mp4

00:32

04 Monte Carlo Method

001 The setting.mp4

01:18

002 Exploration and exploitation.mp4

02:23

003 Monte Carlo prediction.mp4

01:41

004 First visit and every visit MC prediction.mp4

01:39

005 Monte Carlo control.mp4

00:51

006 Additional modifications.mp4

02:55

05 Temporal Difference Methods

001 The setting.mp4

01:10

002 SARSA.mp4

04:37

003 SARSAMAX (Q-learning).mp4

02:02

004 Expected SARSA.mp4

01:46

06 Modified Forms of Reinforcement

001 Deep reinforcement learning.mp4

01:20

002 Multi-agent reinforcement learning.mp4

01:05

003 Inverse reinforcement learning.mp4

01:03

07 Conclusion

001 Your reinforcement learning journey.mp4

01:43

Description

Innovations in finance, health, robotics, and a variety of other sectors have been made possible with reinforcement learning (RL), which involves the training of machines to learn from their environment. Many top tech companies are investing heavily in this field. In this course, instructor Khaulat Abdulhakeem helps you learn the basics of this relatively new, but valuable skill. Get to know the key terminology used in RL, how RL plays a major role in the advancement of AI, and the kinds of problems you can use RL to solve. Khaulat shows you how to define and represent reinforcement learning problems. She also delves into RL algorithms, including the Monte Carlo and temporal difference methods. Plus, she explores deep and multi-agent RL, as well as how inverse learning works and how it can help agents learn by imitation.

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