English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 0h 45m | 190 MB
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.
Table of Contents
1 Reinforcement learning in a nutshell
2 Terms in reinforcement learning
3 A basic RL problem
4 Markov decision process
5 A basic RL solution
6 Monte Carlo method
7 Temporal difference methods
8 Other RL algorithms
9 The setting
10 Exploration and exploitation
11 Monte Carlo prediction
12 First visit and every visit MC prediction
13 Monte Carlo control
14 Additional modifications
15 The setting
16 SARSA
17 SARSAMAX (Q-learning)
18 Expected SARSA
19 Deep reinforcement learning
20 Multi-agent reinforcement learning
21 Inverse reinforcement learning
22 Your reinforcement learning journey
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