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Udemy Advanced Reinforcement Learning policy gradient methods

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Udemy Advanced Reinforcement Learning policy gradient methods
Language: English
Category: Other
Size: 733.1 MB
Added: Oct. 23, 2023, 3:03 p.m.
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Files:
  1. Get Bonus Downloads Here.url 183 bytes
  2. 001 Introduction.html 70 bytes
  3. 002 Reinforcement Learning series.html 699 bytes
  4. 003 Google Colab.mp4 5.8 MB
  5. 003 Google Colab_en.vtt 1.7 KB
  6. 004 Where to begin.html 70 bytes
  7. 001 Elements common to all control tasks.mp4 38.7 MB
  8. 001 Elements common to all control tasks_en.vtt 6.0 KB
  9. 002 The Markov decision process (MDP).mp4 25.1 MB
  10. 002 The Markov decision process (MDP)_en.vtt 5.6 KB
  11. 003 Types of Markov decision process.mp4 8.7 MB
  12. 003 Types of Markov decision process_en.vtt 2.2 KB
  13. 004 Trajectory vs episode.mp4 4.9 MB
  14. 004 Trajectory vs episode_en.vtt 1.1 KB
  15. 005 Reward vs Return.mp4 5.3 MB
  16. 005 Reward vs Return_en.vtt 1.6 KB
  17. 006 Discount factor.mp4 14.8 MB
  18. 006 Discount factor_en.vtt 4.1 KB
  19. 007 Policy.mp4 7.4 MB
  20. 007 Policy_en.vtt 2.1 KB
  21. 008 State values v(s) and action values q(s,a).mp4 4.3 MB
  22. 008 State values v(s) and action values q(s,a)_en.vtt 1.2 KB
  23. 009 Bellman equations.mp4 12.4 MB
  24. 009 Bellman equations_en.vtt 3.0 KB
  25. 010 Solving a Markov decision process.mp4 14.1 MB
  26. 010 Solving a Markov decision process_en.vtt 3.2 KB
  27. 001 Monte Carlo methods.mp4 13.7 MB
  28. 001 Monte Carlo methods_en.vtt 3.3 KB
  29. 002 Solving control tasks with Monte Carlo methods.mp4 23.8 MB
  30. 002 Solving control tasks with Monte Carlo methods_en.vtt 7.0 KB
  31. 003 On-policy Monte Carlo control.mp4 20.4 MB
  32. 003 On-policy Monte Carlo control_en.vtt 4.6 KB
  33. 001 Temporal difference methods.mp4 12.6 MB
  34. 001 Temporal difference methods_en.vtt 3.6 KB
  35. 002 Solving control tasks with temporal difference methods.mp4 14.5 MB
  36. 002 Solving control tasks with temporal difference methods_en.vtt 3.6 KB
  37. 003 Monte Carlo vs temporal difference methods.mp4 8.9 MB
  38. 003 Monte Carlo vs temporal difference methods_en.vtt 1.6 KB
  39. 004 SARSA.mp4 17.8 MB
  40. 004 SARSA_en.vtt 3.9 KB
  41. 005 Q-Learning.mp4 11.1 MB
  42. 005 Q-Learning_en.vtt 2.5 KB
  43. 006 Advantages of temporal difference methods.mp4 3.7 MB
  44. 006 Advantages of temporal difference methods_en.vtt 1.2 KB
  45. 001 N-step temporal difference methods.mp4 12.5 MB
  46. 001 N-step temporal difference methods_en.vtt 3.4 KB
  47. 002 Where do n-step methods fit.mp4 11.1 MB
  48. 002 Where do n-step methods fit_en.vtt 2.7 KB
  49. 003 Effect of changing n.mp4 28.0 MB
  50. 003 Effect of changing n_en.vtt 4.6 KB
  51. 001 Function approximators.mp4 36.3 MB
  52. 001 Function approximators_en.vtt 8.6 KB
  53. 002 Artificial Neural Networks.mp4 24.4 MB
  54. 002 Artificial Neural Networks_en.vtt 3.9 KB
  55. 003 Artificial Neurons.mp4 25.6 MB
  56. 003 Artificial Neurons_en.vtt 5.8 KB
  57. 004 How to represent a Neural Network.mp4 38.2 MB
  58. 004 How to represent a Neural Network_en.vtt 7.3 KB
  59. 005 Stochastic Gradient Descent.mp4 49.8 MB
  60. 005 Stochastic Gradient Descent_en.vtt 6.4 KB
  61. 006 Neural Network optimization.mp4 23.4 MB
  62. 006 Neural Network optimization_en.vtt 4.4 KB
  63. 001 Policy gradient methods.mp4 21.7 MB
  64. 001 Policy gradient methods_en.vtt 4.7 KB
  65. 002 Representing policies using neural networks.mp4 27.8 MB
  66. 002 Representing policies using neural networks_en.vtt 5.2 KB
  67. 003 Policy performance.mp4 8.5 MB
  68. 003 Policy performance_en.vtt 2.6 KB
  69. 004 The policy gradient theorem.mp4 15.9 MB
  70. 004 The policy gradient theorem_en.vtt 3.8 KB
  71. 005 REINFORCE.mp4 13.2 MB
  72. 005 REINFORCE_en.vtt 4.1 KB
  73. 006 Parallel learning.mp4 12.3 MB
  74. 006 Parallel learning_en.vtt 3.6 KB
  75. 007 Entropy regularization.mp4 23.2 MB
  76. 007 Entropy regularization_en.vtt 6.6 KB
  77. 008 REINFORCE 2.mp4 10.9 MB
  78. 008 REINFORCE 2_en.vtt 2.4 KB
  79. 001 PyTorch Lightning.mp4 32.0 MB
  80. 001 PyTorch Lightning_en.vtt 9.3 KB
  81. 002 Link to the code notebook.html 70 bytes
  82. 001 REINFORCE for continuous action spaces.html 70 bytes
  83. 001 A2C.mp4 50.1 MB
  84. 001 A2C_en.vtt 10.6 KB
  85. 001 Generalized Advantage Estimation.html 70 bytes
  86. 001 Proximal Policy Optimization.html 70 bytes
  87. 001 Phasic PPO.html 70 bytes
  88. Bonus Resources.txt 386 bytes

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