Course Content
01 – Introduction
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001 What You Will Learn in this Course.mp4
03:41
002 Required Background, Software, and Hardware.mp4
03:17
003 How to Succeed in this Course.mp4
03:51
02 – Fundamentals of Reinforcement Learning
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001 Review of Fundamental Concepts.mp4
10:27
003 Teaching an AI about Black Jack with Monte Carlo Prediction.mp4
20:00
004 Teaching an AI How to Play Black Jack with Monte Carlo Control.mp4
19:41
005 Review of Temporal Difference Learning Methods.mp4
03:50
006 Teaching an AI about Balance with TD(0) Prediction.mp4
09:42
007 Teaching an AI to Balance the Cart Pole with Q Learning.mp4
24:21
03 – Landing on the Moon with Policy Gradients & Actor Critic Methods
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001 What’s so Great About Policy Gradient Methods.mp4
07:38
002 Combining Neural Networks with Monte Carlo REINFORCE Policy Gradient Algorithm.mp4
05:02
003 Introducing the Lunar Lander Environment.mp4
03:54
004 Coding the Agent’s Brain The Policy Gradient Network.mp4
05:29
005 Coding the Policy Gradient Agent’s Basic Functionality.mp4
05:50
006 Coding the Agent’s Learn Function.mp4
06:04
007 Coding the Policy Gradient Main Loop and Watching our Agent Land on the Moon.mp4
09:27
008 Actor Critic Learning Combining Policy Gradients & Temporal Difference Learning.mp4
04:12
009 Coding the Actor Critic Networks.mp4
03:23
010 Coding the Actor Critic Agent.mp4
08:20
011 Coding the Actor Critic Main Loop and Watching Our Agent Land on the Moon.mp4
09:22
04 – Deep Deterministic Policy Gradients (DDPG) Actor Critic with Continuous Actions
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001 Getting up to Speed With Deep Q Learning.mp4
04:44
002 How to Read and Understand Cutting Edge Research Papers.mp4
06:11
003 Analyzing the DDPG Paper Abstract and Introduction.mp4
07:00
004 Analyzing the Background Material.mp4
05:55
005 What Algorithm Are We Going to Implement.mp4
08:03
006 What Results Should We Expect.mp4
09:36
007 What Other Solutions are Out There.mp4
04:31
008 What Model Architecture and Hyperparameters Do We Need.mp4
03:12
009 Handling the Explore-Exploit Dilemma Coding the OU Action Noise Class.mp4
03:37
010 Giving our Agent a Memory Coding the Replay Memory Buffer Class.mp4
07:04
011 Deep Q Learning for Actor Critic Methods Coding the Critic Network.mp4
15:49
012 Coding the Actor Network Class.mp4
10:10
013 Giving our DDPG Agent Simple Autonomy Coding the Basic Functions.mp4
12:11
014 Giving our DDPG Agent a Brain Coding the Agent’s Learn Function.mp4
09:43
015 Coding the Network Parameter Update Functionality.mp4
08:16
016 Coding the Main Loop and Watching Our DDPG Agent Land on the Moon.mp4
13:11
05 – Twin Delayed Deep Deterministic Policy Gradients (TD3)
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001 Some Tips on Reading this Paper.mp4
01:39
002 Analyzing the TD3 Paper Abstract and Introduction.mp4
09:32
003 What Other Solutions Have People Tried.mp4
03:36
004 Reviewing the Fundamental Concepts.mp4
02:53
005 Is Overestimation Bias Even a Problem in Actor-Critic Methods.mp4
13:16
006 Why is Variance a Problem for Actor-Critic Methods.mp4
06:56
007 What Results Can We Expect.mp4
06:06
008 Coding the Brains of the TD3 Agent – The Actor and Critic Network Classes.mp4
13:34
009 Giving our TD3 Agent Simple Autonomy – Coding the Basic Agent Functionality.mp4
10:57
010 Giving our TD3 Agent a Brain – Coding the Learn Function.mp4
10:31
011 Coding the Network Parameter Update Functionality.mp4
11:32
012 Coding the Main Loop And Watching our Agent Learn to Walk.mp4
09:44
06 – Soft Actor Critic
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001 A Quick Word on the Paper.mp4
00:59
002 Getting Acquainted With a New Framework.mp4
05:45
003 Checking Out What Has Been Done Before.mp4
04:44
004 Inspecting the Foundation of this New Framework.mp4
03:37
005 Digging Into the Mathematics of Soft Actor Critic.mp4
11:00
006 Seeing How the New Algorithm Measures Up.mp4
07:50
007 Coding the Neural Networks.mp4
23:25
008 Coding the Soft Actor Critic Basic Functionality.mp4
10:59
009 Coding the Soft Actor Critic Algorithm.mp4
12:34
010 Coding the Main Loop and Evaluating Our Agent.mp4
12:33
07 – Tensorflow 2 Implementation
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001 Coding the Policy Gradient Network in Tensorflow 2.mp4
03:54
002 Coding the REINFORCE Agent in Tensorflow 2.mp4
10:46
003 Coding the REINFORCE Main Loop and Evaluating our Agent.mp4
08:34
004 Coding the Actor Critic Network in Tensorflow 2.mp4
01:45
005 Coding the Actor Critic Agent in Tensorflow 2.mp4
05:50
006 Coding the Actor Critic Main Program and Evaluating our Agent.mp4
03:39
007 Coding the DDPG Networks in Tensorflow 2.mp4
03:55
008 Coding the DDPG Agent in Tensorflow 2.mp4
15:47
009 Coding the DDPG Main Program and Evaluating our Agent.mp4
04:17
010 Coding the TD3 Agent in Tensorflow 2.mp4
15:58
011 Coding the TD3 Main Program and Evaluating our Agent.mp4
01:53
012 Coding the SAC Networks in Tensorflow 2.mp4
04:32
013 Coding the SAC Agent in Tensorflow 2.mp4
19:15
014 Coding the SAC Main Function and Evaluating our Agent.mp4
02:44
08 – Appendix
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001 Setting Up Our Virtual Environment for the New OpenAI Gym.mp4
09:09
002 Making our Agents Compliant With the New Gym Interface.mp4
19:26
[Last updated 8/2023] Modern Reinforcement Learning: Actor-Critic Agents (Udemy – Engsub)
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