-
Chapter 1 : Introduction 2
-
DocumentLecture1.1
-
Introduction (ดูตัวอย่างฟรี)Lecture1.2
-
-
Chapter 2 : Fundamentals of RL 5
-
Core ConceptsLecture2.1
-
Markov Decision ProcessLecture2.2
-
Episodic and Continuing TasksLecture2.3
-
The Reward HypothesisLecture2.4
-
Discounted ReturnLecture2.5
-
-
Chapter 3 : RL Algorithms 4
-
PolicyLecture3.1
-
State-Value FunctionLecture3.2
-
Action-Value FunctionLecture3.3
-
RL AlgorithmsLecture3.4
-
-
Chapter 4 : Q-learning 4
-
Introduction to Q-LearningLecture4.1
-
Epsilon GreedyLecture4.2
-
Q-Table ImprovementLecture4.3
-
Q-Learning ExamplesLecture4.4
-
-
Chapter 5 : Deep Q-Networks 6
-
Introduction to DQNLecture5.1
-
Deep LearningLecture5.2
-
Deep Q NetworkLecture5.3
-
Fixed Q-TargetsLecture5.4
-
Experience ReplayLecture5.5
-
DQN AlgorithmsLecture5.6
-
-
Chapter 6 : DQN Code 7
-
Breakout EnvironmentLecture6.1
-
Import Libraries and Environment SetupLecture6.2
-
DQN ImplementationLecture6.3
-
Training DQNLecture6.4
-
Update DQNLecture6.5
-
Step-by-Step WalkthroughLecture6.6
-
Code ExecutionLecture6.7
-
This content is protected, please login and enroll course to view this content!