Mastering Deep Q-Learning with GYM-FrozenLake Environment

سرفصل های دوره

From Theory to Practice: A Comprehensive Guide to Deep Q-Learning and the Bellman Equation

1. Introduction

1. Introduction

2. Course Content

1.1 1.zip

1. 1 Structure of the First Project

2.1 2.zip

2. 2 Understand How Bellman Equation Works

3.1 3.zip

3. 3 Understand Why We are Using GYM Library

4.1 4.zip

4. 4 Get the state and action numbers with code

5.1 5.zip

5. 5 Understand Why we are using deque

6.1 6.zip

6. 6 Understand the Q-Table

7.1 7.zip

7. 7 Understand Exploration and Exploitation trade-off

8.1 8.zip

8. 8 Choose an action based on the current observation

9.1 9.zip

9. 9 Apply the action and get the next observation, reward, and done flag

10.1 10.zip

10. 10 Store the experience in the deque and Update the Q-table

11.1 11.zip

11. 11 Make the Agent take action according to Q-Table

12.1 12.zip

12. 12 Solve FrozenLake 8x8 map

13.1 13.zip

13. 13 Understand how deep learning works

14. 14 Using value 1 for Learning Rate in Bellman equation

15. 15 Simplify the Bellman Eq

16. 16 Input Size

17. 17 The Logic of the optimizing parameters of DQN Model

18.1 18.zip

18. 18 Define the model and print weight and bias

19.1 19.zip

19. 19 Learn how to calculate the output with funtions

20.1 20.zip

20. 20 Define Hyperparameters

21.1 21 ADAM OPTIMIZER.pdf

21. 21 Understand the Math of ADAM OPTIMIZER

22.1 22.zip

22. 22 Find best action with the model

23.1 23.zip

23. 23 Make training time shorter

24.1 24.zip

24. 24 Taking sample from memory to optimize the model

25.1 25.zip

25. 25 Learn how to optimize

26.1 26.zip

26. 26 Show the performance of the Model

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تولید کننده: Udemy