Modern Reinforcement Learning: Deep Q Learning in PyTorch

سرفصل های دوره

How to Turn Deep Reinforcement Learning Research Papers Into Agents That Beat Classic Atari Games

1 - Introduction

1 - M1.pdf

1 - What You Will Learn In This Course

2 - M2.pdf

2 - Required Background software and hardware

3 - How to Succeed in this Course

2 - Fundamentals of Reinforcement Learning

4 - Agents Environments and Actions

4 - M4.pdf

5 - M5.pdf

5 - Markov Decision Processes

6 - M6.pdf

6 - Value Functions Action Value Functions and the Bellman Equation

7 - M7.pdf

7 - Model Free vs Model Based Learning

8 - M8.pdf

8 - The ExploreExploit Dilemma

9 - M9.pdf

9 - Temporal Difference Learning

3 - Deep Learning Crash Course

10 - Dealing with Continuous State Spaces with Deep Neural Networks

10 - M10.pdf

11 - M11.pdf

11 - Naive Deep Q Learning in Code Step 1 Coding the Deep Q Network

12 - Naive Deep Q Learning in Code Step 2 Coding the Agent Class

13 - Naive Deep Q Learning in Code Step 3 Coding the Main Loop and Learning

14 - Naive Deep Q Learning in Code Step 4 Verifying the Functionality of Our Code

15 - Naive Deep Q Learning in Code Step 5 Analyzing Our Agents Performance

16 - Dealing with Screen Images with Convolutional Neural Networks

16 - M12.pdf

4 - Human Level Control Through Deep Reinforcement Learning From Paper to Code

17 - How to Read Deep Learning Papers

17 - M13.pdf

18 - Analyzing the Paper

18 - human level control through deep reinforcement learning nature paper.zip

19 - How to Modify the OpenAI Gym Atari Environments

19 - M15.pdf

20 - How to Preprocess the OpenAI Gym Atari Screen Images

21 - How to Stack the Preprocessed Atari Screen Images

22 - How to Combine All the Changes

23 - How to Add Reward Clipping Fire First and No Ops

24 - How to Code the Agents Memory

24 - M16a.pdf

25 - How to Code the Deep Q Network

25 - M16b.pdf

26 - Coding the Deep Q Agent Step 1 Coding the Constructor

26 - M16c.pdf

27 - Coding the Deep Q Agent Step 2 EpsilonGreedy Action Selection

28 - Coding the Deep Q Agent Step 3 Memory Model Saving and Network Copying

29 - Coding the Deep Q Agent Step 4 The Agents Learn Function

30 - Coding the Deep Q Agent Step 5 The Main Loop and Analyzing the Performance

5 - Deep Reinforcement Learning with Double Q Learning

31 - Analyzing the Paper

31 - deep reinforcement learning with double q learning.zip

32 - Coding the Double Q Learning Agent and Analyzing Performance

32 - M18.pdf

6 - Dueling Network Architectures for Deep Reinforcement Learning

33 - Analyzing the Paper

33 - dueling network architectures for deep reinforcement learning the paper.zip

34 - Coding the Dueling Deep Q Network

34 - M20.pdf

35 - Coding the Dueling Deep Q Learning Agent and Analyzing Performance

35 - M21a.pdf

36 - Coding the Dueling Double Deep Q Learning Agent and Analyzing Performance

36 - M21b.pdf

7 - Improving On Our Solutions

37 - Implementing a Command Line Interface for Rapid Model Testing

37 - M22.pdf

38 - Consolidating Our Code Base for Maximum Extensability

38 - M23.pdf

39 - How to Test Our Agent and Watch it Play the Game in Real Time

8 - Conclusion

40 - M24.pdf

40 - Summarizing What Weve Learned

9 - Bonus Lecture

41 - Bonus Video Where to Go From Here

41 - M25.pdf

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تولید کننده: Udemy-Training