content.raw: 8 Free Sources on Reinforcement Learning
content.type: text
content.value: 8 Free Sources on Reinforcement Learning
content.type: new_line
content.type: new_line
content.raw: With the phenomenon of DeepSeek-R1's top reasoning capabilities, we all saw the true power of RL. At its core, RL is a type of machine learning where a model/agent learns to make decisions by interacting with an environment to maximize a reward. RL learns through trial and error, receiving feedback in the form of rewards or penalties.
content.type: text
content.value: With the phenomenon of DeepSeek-R1's top reasoning capabilities, we all saw the true power of RL. At its core, RL is a type of machine learning where a model/agent learns to make decisions by interacting with an environment to maximize a reward. RL learns through trial and error, receiving feedback in the form of rewards or penalties.
content.type: new_line
content.type: new_line
content.raw: Here's a list of free sources that will help you dive into RL and how to use it:
content.type: text
content.value: Here's a list of free sources that will help you dive into RL and how to use it:
content.type: new_line
content.type: new_line
content.raw: 1. "Reinforcement Learning: An Introduction" book by Richard S. Sutton and Andrew G. Barto -> 
content.type: text
content.value: 1. "Reinforcement Learning: An Introduction" book by Richard S. Sutton and Andrew G. Barto -> 
content.href: https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf
content.raw: https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf
content.type: link
content.type: new_line
content.type: new_line
content.raw: 2. Hugging Face Deep Reinforcement Learning Course -> 
content.type: text
content.value: 2. Hugging Face Deep Reinforcement Learning Course -> 
content.href: https://huggingface.co/learn/deep-rl-course/unit0/introduction
content.raw: https://huggingface.co/learn/deep-rl-course/unit0/introduction
content.type: link
content.type: text
content.type: new_line
content.raw: You'll learn how to train agents in unique environments, using best libraries, share your results, compete in challenges, and earn a certificate.
content.type: text
content.value: You'll learn how to train agents in unique environments, using best libraries, share your results, compete in challenges, and earn a certificate.
content.type: new_line
content.type: new_line
content.raw: 3. OpenAI Spinning Up in Deep RL -> 
content.type: text
content.value: 3. OpenAI Spinning Up in Deep RL -> 
content.href: https://spinningup.openai.com/en/latest/index.html
content.raw: https://spinningup.openai.com/en/latest/index.html
content.type: link
content.type: text
content.type: new_line
content.raw: A comprehensive overview of RL with many useful resources
content.type: text
content.value: A comprehensive overview of RL with many useful resources
content.type: new_line
content.type: new_line
content.raw: 4. "Reinforcement Learning and Optimal Control" books, video lectures and course material by Dimitri P. Bertsekas from ASU -> 
content.type: text
content.value: 4. "Reinforcement Learning and Optimal Control" books, video lectures and course material by Dimitri P. Bertsekas from ASU -> 
content.href: https://web.mit.edu/dimitrib/www/RLbook.html
content.raw: https://web.mit.edu/dimitrib/www/RLbook.html
content.type: link
content.type: text
content.type: new_line
content.raw: Explores approximate Dynamic Programming (DP) and RL with key concepts and methods like rollout, tree search, and neural network training for RL and more.
content.type: text
content.value: Explores approximate Dynamic Programming (DP) and RL with key concepts and methods like rollout, tree search, and neural network training for RL and more.
content.type: new_line
content.type: new_line
content.raw: 5. RL Course by David Silver (Google DeepMind) -> 
content.type: text
content.value: 5. RL Course by David Silver (Google DeepMind) -> 
content.href: https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPeb
content.raw: https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPeb
content.type: link
content.type: new_line
content.raw: Many recommend these video lectures as a good foundation
content.type: text
content.value: Many recommend these video lectures as a good foundation
content.type: new_line
content.type: new_line
content.raw: 6. RL theory seminars -> 
content.type: text
content.value: 6. RL theory seminars -> 
content.href: https://sites.google.com/view/rltheoryseminars/home?authuser=0
content.raw: https://sites.google.com/view/rltheoryseminars/home?authuser=0
content.type: link
content.type: text
content.type: new_line
content.raw: Provides virtual seminars from different experts about RL advancements
content.type: text
content.value: Provides virtual seminars from different experts about RL advancements
content.type: new_line
content.type: new_line
content.raw: 7. "Reinforcement Learning Specialization" (a 4-course series on Coursera) -> 
content.type: text
content.value: 7. "Reinforcement Learning Specialization" (a 4-course series on Coursera) -> 
content.href: https://www.coursera.org/learn/fundament
content.raw: https://www.coursera.org/learn/fundament
content.type: link
content.type: new_line
content.type: new_line
content.raw: 8. Concepts: RLHF, RLAIF, RLEF, RLCF -> 
content.type: text
content.value: 8. Concepts: RLHF, RLAIF, RLEF, RLCF -> 
content.href: https://www.turingpost.com/p/rl-f
content.raw: https://www.turingpost.com/p/rl-f
content.type: link
content.type: text
content.type: new_line
content.raw: Our flashcards easily explain what are these four RL approaches with different feedback
content.type: text
content.value: Our flashcards easily explain what are these four RL approaches with different feedback
rawContent: 8 Free Sources on Reinforcement Learning
  
  With the phenomenon of DeepSeek-R1's top reasoning capabilities, we all saw the true power of RL. At its core, RL is a type of machine learning where a model/agent learns to make decisions by interacting with an environment to maximize a reward. RL learns through trial and error, receiving feedback in the form of rewards or penalties.
  
  Here's a list of free sources that will help you dive into RL and how to use it:
  
  1. "Reinforcement Learning: An Introduction" book by Richard S. Sutton and Andrew G. Barto -> https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf
  
  2. Hugging Face Deep Reinforcement Learning Course -> https://huggingface.co/learn/deep-rl-course/unit0/introduction 
  You'll learn how to train agents in unique environments, using best libraries, share your results, compete in challenges, and earn a certificate.
  
  3. OpenAI Spinning Up in Deep RL -> https://spinningup.openai.com/en/latest/index.html 
  A comprehensive overview of RL with many useful resources
  
  4. "Reinforcement Learning and Optimal Control" books, video lectures and course material by Dimitri P. Bertsekas from ASU -> https://web.mit.edu/dimitrib/www/RLbook.html 
  Explores approximate Dynamic Programming (DP) and RL with key concepts and methods like rollout, tree search, and neural network training for RL and more.
  
  5. RL Course by David Silver (Google DeepMind) -> https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPeb
  Many recommend these video lectures as a good foundation
  
  6. RL theory seminars -> https://sites.google.com/view/rltheoryseminars/home?authuser=0 
  Provides virtual seminars from different experts about RL advancements
  
  7. "Reinforcement Learning Specialization" (a 4-course series on Coursera) -> https://www.coursera.org/learn/fundament
  
  8. Concepts: RLHF, RLAIF, RLEF, RLCF -> https://www.turingpost.com/p/rl-f 
  Our flashcards easily explain what are these four RL approaches with different feedback
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