Answers.Body: <p>SAC is known for its stability and efficiency in continuous control tasks. You can directly read the original papers on SAC with detailed derivations and explanations which is Haarnoja et al (2018), <em>Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor</em>.</p>
  <p>There's an extended version including additional details, practical implementation considerations, and applications in robotics. Haarnoja et al, (2019), <em><a href="https://arxiv.org/abs/1812.05905" rel="nofollow noreferrer">Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor</a></em>.</p>
  <p>Lapan (2020), <em><a href="https://www.abebooks.com/servlet/BookDetailsPL?bi=31956042954&amp;dest=usa&amp;ref_=ps_ms_370718797&amp;cm_mmc=msn-_-comus_shopp_textbook-_-naa-_-naa&amp;msclkid=d5969c5c0eba108652fafabca644b7c1" rel="nofollow noreferrer">Deep Reinforcement Learning Hands-On</a></em>. This book provides an accessible introduction to reinforcement learning and practical implementations, including SAC on page 618. It explains the method and its underlying theory, with code examples in PyTorch. This is useful if you prefer a practical, code-oriented approach.</p>
  
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Answers.CreationDate: 2024-09-10T20:28:54.560Z
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Answers.UserDisplayName: cinch
Answers.Body: <p>SAC: Soft-Actor critic method is popular especially in the field of robotics as the author had worked on this method during his PhD while working in the field of robotics.</p>
  <p>For SAC, you might find this blog helpful: <a href="http://www.imgeorgiev.com/2023-06-27-sac/#actor-critic-methods" rel="nofollow noreferrer">SAC blog</a></p>
  <p>Prior to SAC if you need to know the intuition behind AC then you can refer to this blog: <a href="https://rohan-v-thorat.github.io/pages/blogs/Deriving-Actor-Critic-method-along-with-some-intuition.html" rel="nofollow noreferrer">AC blog</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-09-13T10:56:44.680Z
Answers.Text: I have read this blog and almost half of the discussion is full of errors.
Answers.UserDisplayName: DSPinfinity
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-09-15T11:48:59.650Z
Answers.Text: @DSPinfinity did you find errors in both the blogs i.e. SAC blog and AC blog? or was it only in SAC blog.
Answers.UserDisplayName: avenger
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Answers.CreationDate: 2024-09-13T05:45:58.043Z
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Answers.UserDisplayName: avenger
Body: <p>I am looking some good material (lecture notes, slides or textbook)
  which explains the soft actor-critic method with the derivations.
  I will be happy for the suggestions.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-09-11T11:05:35.453Z
Comments.Text: Please, define "good" in "good material". Good is very subjective, unless explained.
Comments.UserDisplayName: nbro
Tags: <reinforcement-learning><soft-actor-critic>
Title: Material suggestion for soft actor-critic method
UserDisplayName: DSPinfinity
ThreadText: # Material suggestion for soft actor-critic method
  
  (Asked by: username_0 on 2024-09-10)
  
  I am looking some good material (lecture notes, slides or textbook)
  which explains the soft actor-critic method with the derivations.
  I will be happy for the suggestions.
  
  **Tags:** reinforcement-learning, soft-actor-critic
  
  > **Comments:**
  > **username_1**: Please, define "good" in "good material". Good is very subjective, unless explained.  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_2 on 2024-09-10. Score: 1
  
  SAC is known for its stability and efficiency in continuous control tasks. You can directly read the original papers on SAC with detailed derivations and explanations which is Haarnoja et al (2018), *Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor*.
  
  There's an extended version including additional details, practical implementation considerations, and applications in robotics. Haarnoja et al, (2019), *[Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://arxiv.org/abs/1812.05905)*.
  
  Lapan (2020), *[Deep Reinforcement Learning Hands-On](https://www.abebooks.com/servlet/BookDetailsPL?bi=31956042954&dest=usa&ref_=ps_ms_370718797&cm_mmc=msn-_-comus_shopp_textbook-_-naa-_-naa&msclkid=d5969c5c0eba108652fafabca644b7c1)*. This book provides an accessible introduction to reinforcement learning and practical implementations, including SAC on page 618. It explains the method and its underlying theory, with code examples in PyTorch. This is useful if you prefer a practical, code-oriented approach.
  
  
  ---
  
  ### Answer by username_3 on 2024-09-13. Score: 0
  
  SAC: Soft-Actor critic method is popular especially in the field of robotics as the author had worked on this method during his PhD while working in the field of robotics.
  
  For SAC, you might find this blog helpful: [SAC blog](http://www.imgeorgiev.com/2023-06-27-sac/#actor-critic-methods)
  
  Prior to SAC if you need to know the intuition behind AC then you can refer to this blog: [AC blog](https://rohan-v-thorat.github.io/pages/blogs/Deriving-Actor-Critic-method-along-with-some-intuition.html)
  
  > **Comments:**
  > **username_0**: I have read this blog and almost half of the discussion is full of errors.  
  > **username_3**: @username_0 did you find errors in both the blogs i.e. SAC blog and AC blog? or was it only in SAC blog.  
  
  ---
  
  