Course Content
01 – Introduction
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001 What You Will Learn In This Course.mp4
04:07
002 Required Background, software, and hardware.mp4
03:46
003 How to Succeed in this Course.mp4
04:45
02 – Fundamentals of Reinforcement Learning
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001 Agents, Environments, and Actions.mp4
10:03
002 Markov Decision Processes.mp4
11:30
003 Value Functions, Action Value Functions, and the Bellman Equation.mp4
08:10
004 Model Free vs. Model Based Learning.mp4
03:33
005 The Explore-Exploit Dilemma.mp4
05:27
006 Temporal Difference Learning.mp4
22:01
03 – Deep Learning Crash Course
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001 Dealing with Continuous State Spaces with Deep Neural Networks.mp4
18:53
002 Naive Deep Q Learning in Code Step 1 – Coding the Deep Q Network.mp4
07:55
003 Naive Deep Q Learning in Code Step 2 – Coding the Agent Class.mp4
10:09
004 Naive Deep Q Learning in Code Step 3 – Coding the Main Loop and Learning.mp4
09:21
005 Naive Deep Q Learning in Code Step 4 – Verifying the Functionality of Our Code.mp4
02:12
006 Naive Deep Q Learning in Code Step 5 – Analyzing Our Agent’s Performance.mp4
02:42
007 Dealing with Screen Images with Convolutional Neural Networks.mp4
03:52
04 – Human Level Control Through Deep Reinforcement Learning From Paper to Code
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001 How to Read Deep Learning Papers.mp4
07:15
002 Analyzing the Paper.mp4
20:33
003 How to Modify the OpenAI Gym Atari Environments.mp4
14:29
004 How to Preprocess the OpenAI Gym Atari Screen Images.mp4
02:55
005 How to Stack the Preprocessed Atari Screen Images.mp4
03:26
006 How to Combine All the Changes.mp4
01:30
007 How to Add Reward Clipping, Fire First, and No Ops.mp4
04:49
008 How to Code the Agent’s Memory.mp4
10:55
009 How to Code the Deep Q Network.mp4
11:44
010 Coding the Deep Q Agent Step 1 – Coding the Constructor.mp4
07:48
011 Coding the Deep Q Agent Step 2 – Epsilon-Greedy Action Selection.mp4
02:22
012 Coding the Deep Q Agent Step 3 – Memory, Model.MP4
04:24
013 Coding the Deep Q Agent Step 4 – The Agent’s Learn Function.mp4
07:54
014 Coding the Deep Q Agent Step 5 – The Main Loop and Analyzing.mp4
14:14
05 – Deep Reinforcement Learning with Double Q Learning
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001 Analyzing the Paper.mp4
15:39
002 Coding the Double Q Learning Agent and Analyzing Performance.mp4
08:51
06 – Dueling Network Architectures for Deep Reinforcement Learning
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001 Analyzing the Paper.mp4
14:01
002 Coding the Dueling Deep Q Network.mp4
03:20
003 Coding the Dueling Deep Q Learning Agent and Analyzing Performance.mp4
10:10
004 Coding the Dueling Double Deep Q Learning Agent and Analyzing Performance.mp4
05:36
07 – Improving On Our Solutions
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001 Implementing a Command Line Interface for Rapid Model Testing.mp4
09:30
002 Consolidating Our Code Base for Maximum Extensability.mp4
18:32
003 How to Test Our Agent and Watch it Play the Game in Real Time.mp4
07:39
08 – Conclusion
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001 Summarizing What We’ve Learned.mp4
04:40
09 – Bonus Lecture
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001 Bonus Video Where to Go From Here.mp4
01:12
10 – Tensorflow 2 Implementations
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001 Differences Between Tensorflow 2 and PyTorch.mp4
04:11
002 Coding the Deep Q Network Class in Tensorflow 2.mp4
06:27
003 Coding the Deep Q Learning Agent in Tensorflow 2.mp4
11:08
004 Testing the Tensorflow 2 Deep Q Learning Agent.mp4
05:54
005 Coding the Tensorflow 2 Double Q Learning Agent.mp4
02:35
006 Coding the Dueling Network and Agent in Tensorflow 2.mp4
06:32
007 Coding the Dueling Double DQN Agent in Tensorflow 2.mp4
04:32
11 – Appendix
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001 Installing the New OpenAI Gym in a Virtual Environment.mp4
09:09
002 Making the DQN Agent Compliant with the New Gym Interface.mp4
12:20
[Last updated 8/2023] Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2) (Udemy – Engsub)
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