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[] Coursera - Practical Reinforcement Learning

  • 收录时间:2018-12-09 01:31:25
  • 文件大小:1GB
  • 下载次数:151
  • 最近下载:2020-12-29 18:15:15
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文件列表

  1. 013.Honor/033. Partial observability.mp4 57MB
  2. 019.Planning with Monte Carlo Tree Search/053. Introduction to planning.mp4 52MB
  3. 011.Limitations of Tabular Methods/025. Supervised & Reinforcement Learning.mp4 51MB
  4. 005.Striving for reward/014. Reward design.mp4 50MB
  5. 011.Limitations of Tabular Methods/027. Difficulties with Approximate Methods.mp4 47MB
  6. 018.Uncertainty-based exploration/052. Bayesian UCB.mp4 41MB
  7. 009.On-policy vs off-policy/023. Accounting for exploration. Expected Value SARSA..mp4 38MB
  8. 006.Bellman equations/015. State and Action Value Functions.mp4 37MB
  9. 003.Black box optimization/006. Crossentropy method.mp4 36MB
  10. 014.Policy-based RL vs Value-based RL/034. Intuition.mp4 35MB
  11. 013.Honor/032. More DQN tricks.mp4 34MB
  12. 011.Limitations of Tabular Methods/026. Loss functions in value based RL.mp4 34MB
  13. 001.Welcome/001. Why should you care.mp4 32MB
  14. 007.Generalized Policy Iteration/017. Policy evaluation & improvement.mp4 32MB
  15. 014.Policy-based RL vs Value-based RL/036. Policy gradient formalism.mp4 32MB
  16. 015.REINFORCE/038. REINFORCE.mp4 31MB
  17. 019.Planning with Monte Carlo Tree Search/054. Monte Carlo Tree Search.mp4 31MB
  18. 008.Model-free learning/020. Monte-Carlo & Temporal Difference; Q-learning.mp4 30MB
  19. 012.Case Study Deep Q-Network/029. DQN the internals.mp4 30MB
  20. 008.Model-free learning/019. Model-based vs model-free.mp4 29MB
  21. 008.Model-free learning/021. Exploration vs Exploitation.mp4 28MB
  22. 004.All the cool stuff that isn't in the base track/011. Evolution strategies log-derivative trick.mp4 28MB
  23. 012.Case Study Deep Q-Network/028. DQN bird's eye view.mp4 28MB
  24. 010.Experience Replay/024. On-policy vs off-policy; Experience replay.mp4 27MB
  25. 016.Actor-critic/042. Case study A3C.mp4 26MB
  26. 017.Measuting exploration/045. Recap bandits.mp4 25MB
  27. 016.Actor-critic/039. Advantage actor-critic.mp4 25MB
  28. 007.Generalized Policy Iteration/018. Policy and value iteration.mp4 24MB
  29. 016.Actor-critic/044. Combining supervised & reinforcement learning.mp4 24MB
  30. 002.Reinforcement Learning/004. Decision process & applications.mp4 23MB
  31. 003.Black box optimization/008. More on approximate crossentropy method.mp4 23MB
  32. 018.Uncertainty-based exploration/048. Intuitive explanation.mp4 22MB
  33. 018.Uncertainty-based exploration/051. UCB-1.mp4 22MB
  34. 017.Measuting exploration/046. Regret measuring the quality of exploration.mp4 21MB
  35. 004.All the cool stuff that isn't in the base track/012. Evolution strategies duct tape.mp4 21MB
  36. 004.All the cool stuff that isn't in the base track/009. Evolution strategies core idea.mp4 21MB
  37. 013.Honor/031. Double Q-learning.mp4 20MB
  38. 003.Black box optimization/007. Approximate crossentropy method.mp4 19MB
  39. 013.Honor/030. DQN statistical issues.mp4 19MB
  40. 017.Measuting exploration/047. The message just repeats. 'Regret, Regret, Regret.'.mp4 18MB
  41. 006.Bellman equations/016. Measuring Policy Optimality.mp4 18MB
  42. 003.Black box optimization/005. Markov Decision Process.mp4 18MB
  43. 002.Reinforcement Learning/003. Multi-armed bandit.mp4 18MB
  44. 004.All the cool stuff that isn't in the base track/010. Evolution strategies math problems.mp4 18MB
  45. 016.Actor-critic/040. Duct tape zone.mp4 18MB
  46. 018.Uncertainty-based exploration/049. Thompson Sampling.mp4 17MB
  47. 016.Actor-critic/041. Policy-based vs Value-based.mp4 17MB
  48. 018.Uncertainty-based exploration/050. Optimism in face of uncertainty.mp4 17MB
  49. 014.Policy-based RL vs Value-based RL/035. All Kinds of Policies.mp4 16MB
  50. 004.All the cool stuff that isn't in the base track/013. Blackbox optimization drawbacks.mp4 15MB
  51. 016.Actor-critic/043. A3C case study (2 2).mp4 15MB
  52. 014.Policy-based RL vs Value-based RL/037. The log-derivative trick.mp4 13MB
  53. 001.Welcome/002. Reinforcement learning vs all.mp4 11MB
  54. 008.Model-free learning/022. Footnote Monte-Carlo vs Temporal Difference.mp4 10MB
  55. 013.Honor/033. Partial observability.srt 28KB
  56. 019.Planning with Monte Carlo Tree Search/053. Introduction to planning.srt 25KB
  57. 011.Limitations of Tabular Methods/025. Supervised & Reinforcement Learning.srt 25KB
  58. 005.Striving for reward/014. Reward design.srt 23KB
  59. 011.Limitations of Tabular Methods/027. Difficulties with Approximate Methods.srt 22KB
  60. 018.Uncertainty-based exploration/052. Bayesian UCB.srt 19KB
  61. 006.Bellman equations/015. State and Action Value Functions.srt 18KB
  62. 009.On-policy vs off-policy/023. Accounting for exploration. Expected Value SARSA..srt 17KB
  63. 013.Honor/032. More DQN tricks.srt 16KB
  64. 014.Policy-based RL vs Value-based RL/034. Intuition.srt 16KB
  65. 003.Black box optimization/006. Crossentropy method.srt 16KB
  66. 001.Welcome/001. Why should you care.srt 15KB
  67. 011.Limitations of Tabular Methods/026. Loss functions in value based RL.srt 15KB
  68. 019.Planning with Monte Carlo Tree Search/054. Monte Carlo Tree Search.srt 15KB
  69. 008.Model-free learning/020. Monte-Carlo & Temporal Difference; Q-learning.srt 15KB
  70. 007.Generalized Policy Iteration/017. Policy evaluation & improvement.srt 14KB
  71. 008.Model-free learning/019. Model-based vs model-free.srt 14KB
  72. 015.REINFORCE/038. REINFORCE.srt 14KB
  73. 008.Model-free learning/021. Exploration vs Exploitation.srt 14KB
  74. 014.Policy-based RL vs Value-based RL/036. Policy gradient formalism.srt 13KB
  75. 004.All the cool stuff that isn't in the base track/011. Evolution strategies log-derivative trick.srt 13KB
  76. 012.Case Study Deep Q-Network/029. DQN the internals.srt 12KB
  77. 007.Generalized Policy Iteration/018. Policy and value iteration.srt 12KB
  78. 017.Measuting exploration/045. Recap bandits.srt 12KB
  79. 016.Actor-critic/044. Combining supervised & reinforcement learning.srt 12KB
  80. 016.Actor-critic/039. Advantage actor-critic.srt 12KB
  81. 012.Case Study Deep Q-Network/028. DQN bird's eye view.srt 11KB
  82. 010.Experience Replay/024. On-policy vs off-policy; Experience replay.srt 11KB
  83. 016.Actor-critic/042. Case study A3C.srt 11KB
  84. 018.Uncertainty-based exploration/048. Intuitive explanation.srt 11KB
  85. 003.Black box optimization/008. More on approximate crossentropy method.srt 10KB
  86. 018.Uncertainty-based exploration/051. UCB-1.srt 10KB
  87. 017.Measuting exploration/046. Regret measuring the quality of exploration.srt 10KB
  88. 002.Reinforcement Learning/004. Decision process & applications.srt 10KB
  89. 004.All the cool stuff that isn't in the base track/012. Evolution strategies duct tape.srt 10KB
  90. 013.Honor/031. Double Q-learning.srt 9KB
  91. 013.Honor/030. DQN statistical issues.srt 9KB
  92. 017.Measuting exploration/047. The message just repeats. 'Regret, Regret, Regret.'.srt 9KB
  93. 004.All the cool stuff that isn't in the base track/010. Evolution strategies math problems.srt 9KB
  94. 006.Bellman equations/016. Measuring Policy Optimality.srt 9KB
  95. 003.Black box optimization/005. Markov Decision Process.srt 8KB
  96. 003.Black box optimization/007. Approximate crossentropy method.srt 8KB
  97. 018.Uncertainty-based exploration/049. Thompson Sampling.srt 8KB
  98. 018.Uncertainty-based exploration/050. Optimism in face of uncertainty.srt 8KB
  99. 016.Actor-critic/040. Duct tape zone.srt 8KB
  100. 014.Policy-based RL vs Value-based RL/035. All Kinds of Policies.srt 7KB
  101. 004.All the cool stuff that isn't in the base track/009. Evolution strategies core idea.srt 7KB
  102. 004.All the cool stuff that isn't in the base track/013. Blackbox optimization drawbacks.srt 7KB
  103. 002.Reinforcement Learning/003. Multi-armed bandit.srt 7KB
  104. 016.Actor-critic/041. Policy-based vs Value-based.srt 7KB
  105. 016.Actor-critic/043. A3C case study (2 2).srt 6KB
  106. 014.Policy-based RL vs Value-based RL/037. The log-derivative trick.srt 6KB
  107. 001.Welcome/002. Reinforcement learning vs all.srt 5KB
  108. 008.Model-free learning/022. Footnote Monte-Carlo vs Temporal Difference.srt 5KB
  109. [FTU Forum].url 252B
  110. [FreeCoursesOnline.Me].url 133B
  111. [FreeTutorials.Us].url 119B