Download Udemy - MQL4 Special Course - Two Pairs Arbitrage 2022 Torrent

Torrent Contents Size: 1.7 GB

Udemy - MQL4 Special Course - Two Pairs Arbitrage 2022

Bonus Resources.txt

TXT

102.4 B

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URL

204.8 B

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1 - Introduction

1. Introduction.en_US.srt

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7.4 KB

1. Introduction.mp4

MP4

22.3 MB

2. Reinforcement Learning series.html

HTML

6.9 KB

3. Google Colab.en_US.srt

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1.7 KB

3. Google Colab.mp4

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3.6 MB

4. Where to begin.en_US.srt

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1.2 KB

4. Where to begin.mp4

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2.1 MB

5. Complete code.html

HTML

5.4 KB

6. Connect with me on social media.html

HTML

5.7 KB

__MACOSX

advanced_rl_pg_methods_complete

_2_REINFORCE_continuous.ipynb

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307.2 B

_4_proximal_policy_optimization.ipynb

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307.2 B

_5_generalized_advantage_estimation.ipynb

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307.2 B

_6_TRPO.ipynb

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307.2 B

advanced_rl_pg_methods_complete

10 - Advantage Actor Critic (A2C)

11 - Trust region methods

12 - Proximal Policy Optimization (PPO)

13 - Generalized Advantage Estimation (GAE)

14 - Trust Region Policy Optimization (TRPO)

15 - Final steps

2 - Refresher The Markov Decision Process (MDP)

10. Trajectory vs episode.en_US.srt

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1.1 KB

10. Trajectory vs episode.mp4

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3 MB

11. Reward vs Return.en_US.srt

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1.6 KB

11. Reward vs Return.mp4

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3.2 MB

12. Discount factor.en_US.srt

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4.1 KB

12. Discount factor.mp4

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8.8 MB

13. Policy.en_US.srt

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2.1 KB

13. Policy.mp4

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4.5 MB

14. State values v(s) and action values q(s,a).en_US.srt

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1.2 KB

14. State values v(s) and action values q(s,a).mp4

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2.6 MB

15. Bellman equations.en_US.srt

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3 KB

15. Bellman equations.mp4

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7.5 MB

16. Solving a Markov decision process.en_US.srt

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3.2 KB

16. Solving a Markov decision process.mp4

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8.6 MB

3 - Refresher Monte Carlo methods

17. Monte Carlo methods.en_US.srt

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3.3 KB

17. Monte Carlo methods.mp4

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8.2 MB

18. Solving control tasks with Monte Carlo methods.en_US.srt

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7 KB

18. Solving control tasks with Monte Carlo methods.mp4

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15 MB

19. On-policy Monte Carlo control.en_US.srt

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4.6 KB

19. On-policy Monte Carlo control.mp4

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15.1 MB

4 - Refresher Temporal difference methods

20. Temporal difference methods.en_US.srt

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3.6 KB

20. Temporal difference methods.mp4

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7.7 MB

21. Solving control tasks with temporal difference methods.en_US.srt

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3.6 KB

21. Solving control tasks with temporal difference methods.mp4

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8.9 MB

22. Monte Carlo vs temporal difference methods.en_US.srt

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1.6 KB

22. Monte Carlo vs temporal difference methods.mp4

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5 MB

23. SARSA.en_US.srt

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3.9 KB

23. SARSA.mp4

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10.8 MB

24. Q-Learning.en_US.srt

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2.5 KB

24. Q-Learning.mp4

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6.5 MB

25. Advantages of temporal difference methods.en_US.srt

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1.2 KB

25. Advantages of temporal difference methods.mp4

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2.2 MB

5 - Refresher N-step bootstrapping

26. N-step temporal difference methods.en_US.srt

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3.4 KB

26. N-step temporal difference methods.mp4

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7.5 MB

27. Where do n-step methods fit.en_US.srt

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27. Where do n-step methods fit.mp4

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6.7 MB

28. Effect of changing n.en_US.srt

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28. Effect of changing n.mp4

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15.5 MB

6 - Refresher Brief introduction to Neural Networks

29. Function approximators.en_US.srt

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29. Function approximators.mp4

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32.5 MB

30. Artificial Neural Networks.en_US.srt

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30. Artificial Neural Networks.mp4

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13.3 MB

31. Artificial Neurons.en_US.srt

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31. Artificial Neurons.mp4

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39.1 MB

32. How to represent a Neural Network.en_US.srt

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7.3 KB

32. How to represent a Neural Network.mp4

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21.8 MB

33. Stochastic Gradient Descent.en_US.srt

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6.4 KB

33. Stochastic Gradient Descent.mp4

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39.5 MB

34. Neural Network optimization.en_US.srt

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4.4 KB

34. Neural Network optimization.mp4

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12.5 MB

7 - Refresher REINFORCE

35. Policy gradient methods.en_US.srt

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4.7 KB

35. Policy gradient methods.mp4

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12.7 MB

36. Representing policies using neural networks.en_US.srt

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5.2 KB

36. Representing policies using neural networks.mp4

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13.4 MB

37. Policy performance.en_US.srt

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2.6 KB

37. Policy performance.mp4

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10.2 MB

38. The policy gradient theorem.en_US.srt

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3.8 KB

38. The policy gradient theorem.mp4

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13.6 MB

39. REINFORCE.en_US.srt

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4.1 KB

39. REINFORCE.mp4

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8.1 MB

40. Parallel learning.en_US.srt

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3.6 KB

40. Parallel learning.mp4

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7.6 MB

41. Entropy regularization.en_US.srt

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6.6 KB

41. Entropy regularization.mp4

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13.8 MB

42. REINFORCE 2.en_US.srt

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2.4 KB

42. REINFORCE 2.mp4

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6.4 MB

8 - PyTorch Lightning

43. PyTorch Lightning.en_US.srt

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9.3 KB

43. PyTorch Lightning.mp4

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23.9 MB

44. Link to the code notebook.html

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5.6 KB

45. Create the policy.en_US.srt

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13.7 KB

45. Create the policy.mp4

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95.9 MB

46. Create the environment.en_US.srt

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9.6 KB

46. Create the environment.mp4

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27.3 MB

47. Create the dataset.en_US.srt

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12.2 KB

47. Create the dataset.mp4

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39.5 MB

48. Create the REINFORCE algorithm - Part 1.en_US.srt

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6.2 KB

48. Create the REINFORCE algorithm - Part 1.mp4

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21 MB

49. Create the REINFORCE algorithm - Part 2.en_US.srt

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10.1 KB

49. Create the REINFORCE algorithm - Part 2.mp4

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42 MB

50. Check the resulting agent.en_US.srt

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6.2 KB

50. Check the resulting agent.mp4

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39.4 MB

9 - REINFORCE for continuous control tasks

51. REINFORCE for continuous action spaces.en_US.srt	SRT	5.8 KB
51. REINFORCE for continuous action spaces.mp4	MP4	9.7 MB
52. Link to the code notebook.html	HTML	5.6 KB
53. Create the policy.en_US.srt	SRT	10.3 KB
53. Create the policy.mp4	MP4	64 MB
54. Create the inverted pendulum environment.en_US.srt	SRT	7.8 KB
54. Create the inverted pendulum environment.mp4	MP4	33.8 MB
55. Create the dataset.en_US.srt	SRT	6.7 KB
55. Create the dataset.mp4	MP4	25.6 MB
56. Creating the algorithm - Part 1.en_US.srt	SRT	5.5 KB
56. Creating the algorithm - Part 1.mp4	MP4	20 MB
57. Creating the algorithm - Part 2.en_US.srt	SRT	5.6 KB
57. Creating the algorithm - Part 2.mp4	MP4	27.6 MB
58. Check the resulting agent.en_US.srt	SRT	2.3 KB
58. Check the resulting agent.mp4	MP4	7.2 MB

7. Elements common to all control tasks.en_US.srt

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6 KB

7. Elements common to all control tasks.mp4

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21.5 MB

8. The Markov decision process (MDP).en_US.srt

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5.6 KB

8. The Markov decision process (MDP).mp4

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15.2 MB

9. Types of Markov decision process.en_US.srt

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2.2 KB

9. Types of Markov decision process.mp4

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5.2 MB

96. Final steps.html

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6 KB

97. Connect with me on social media.html

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5.7 KB

87. Trust region policy optimization 1.en_US.srt

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3.9 KB

87. Trust region policy optimization 1.mp4

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6.5 MB

88. Trust region policy optimization 2.en_US.srt

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6.2 KB

88. Trust region policy optimization 2.mp4

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11.1 MB

89. Link to the code notebook.html

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5.6 KB

90. TRPO in code - Part 1.en_US.srt

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3.5 KB

90. TRPO in code - Part 1.mp4

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19 MB

91. TRPO in code - Part 2.en_US.srt

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2.5 KB

91. TRPO in code - Part 2.mp4

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11 MB

92. TRPO in code - Part 3.en_US.srt

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2.1 KB

92. TRPO in code - Part 3.mp4

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6.8 MB

93. TRPO in code - Part 4.en_US.srt

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4.7 KB

93. TRPO in code - Part 4.mp4

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19.7 MB

94. TRPO in code - Part 5.en_US.srt

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8.9 KB

94. TRPO in code - Part 5.mp4

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43.1 MB

95. TRPO in code - Part 6.en_US.srt

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921.6 B

95. TRPO in code - Part 6.mp4

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6.7 MB

80. Generalized Advantage Estimation.en_US.srt

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12.5 KB

80. Generalized Advantage Estimation.mp4

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21.2 MB

81. Link to the code notebook.html

HTML

5.6 KB

82. Create the Half Cheetah environment.en_US.srt

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5 KB

82. Create the Half Cheetah environment.mp4

MP4

38.7 MB

83. Create the dataset.en_US.srt

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10 KB

83. Create the dataset.mp4

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40.1 MB

84. PPO with generalized advantage estimation - Part 1.en_US.srt

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3.3 KB

84. PPO with generalized advantage estimation - Part 1.mp4

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15.3 MB

85. PPO with generalized advantage estimation - Part 2.en_US.srt

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5.2 KB

85. PPO with generalized advantage estimation - Part 2.mp4

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33.8 MB

86. Checking the resulting agent.en_US.srt

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1 KB

86. Checking the resulting agent.mp4

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10.4 MB

73. Proximal Policy Optimization.en_US.srt

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9.9 KB

73. Proximal Policy Optimization.mp4

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20.8 MB

74. Link to the code notebook.html

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5.6 KB

75. Create the environment.en_US.srt

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7.8 KB

75. Create the environment.mp4

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61.2 MB

76. Create the dataset.en_US.srt

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6.7 KB

76. Create the dataset.mp4

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26.4 MB

77. Create the PPO algorithm - Part 1.en_US.srt

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4.9 KB

77. Create the PPO algorithm - Part 1.mp4

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29.2 MB

78. Create the PPO algorithm - Part 2.en_US.srt

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10.2 KB

78. Create the PPO algorithm - Part 2.mp4

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91.6 MB

79. Check the resulting agent.en_US.srt

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1.9 KB

79. Check the resulting agent.mp4

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13.2 MB

67. Line search vs trust region methods.en_US.srt

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2.6 KB

67. Line search vs trust region methods.mp4

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4.2 MB

68. Line search methods.en_US.srt

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7.2 KB

68. Line search methods.mp4

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20.5 MB

69. Trust region methods 1.en_US.srt

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3.4 KB

69. Trust region methods 1.mp4

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8.9 MB

70. Kullback-Leibler divergence.en_US.srt

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4.7 KB

70. Kullback-Leibler divergence.mp4

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8.2 MB

71. Trust region methods 2.en_US.srt

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11.4 KB

71. Trust region methods 2.mp4

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20.3 MB

72. Trust region methods 3.en_US.srt

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3.1 KB

72. Trust region methods 3.mp4

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4.9 MB

59. A2C.en_US.srt

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10.6 KB

59. A2C.mp4

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29.2 MB

60. Link to the code notebook.html

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5.6 KB

61. Create the policy and value network.en_US.srt

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4.5 KB

61. Create the policy and value network.mp4

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27 MB

62. Create the environment.en_US.srt

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5.9 KB

62. Create the environment.mp4

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17.4 MB

63. Create the dataset.en_US.srt

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2.5 KB

63. Create the dataset.mp4

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10 MB

64. Implement A2C - Part 1.en_US.srt

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4.9 KB

64. Implement A2C - Part 1.mp4

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19.1 MB

65. Implement A2C - Part 2.en_US.srt

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8.9 KB

65. Implement A2C - Part 2.mp4

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51.5 MB

66. Check the resulting agent.en_US.srt

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2.3 KB

66. Check the resulting agent.mp4

MP4

19.2 MB

1_REINFORCE.ipynb

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15.5 KB

2_REINFORCE_continuous.ipynb

IPYNB

20.9 KB

3_advantage_actor_critic.ipynb

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14.8 KB

4_proximal_policy_optimization.ipynb

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20.3 KB

5_generalized_advantage_estimation.ipynb

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21.2 KB

6_TRPO.ipynb

IPYNB

29 KB

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