Many users thank Fabian Pedregosa for resuming his blog series deriving REINFORCE estimators and policy gradients because it offers clear first-principles explanations of RL concepts.
Based on 6 visible X reactions from 7 accounts; directional sample.
Ask a question below.
Published answers will appear here.
@fpedregosa Love seeing RL concepts explained from first principles instead of skipping the fundamentals.
@fpedregosa So glad you are blogging again Fabian!
@fpedregosa thankyou had a good revision
@fpedregosa Great work! 😊
@fpedregosa Love seeing RL concepts explained from first principles instead of skipping the fundamentals.
Starting a new blog post series to better understand modern RL algorithms from the ground up. Part 1 covers the classic REINFORCE estimator: deriving unbiased policy gradients without differentiating through the environment, and analyzing its variance: http://fa.bianp.net/blog/2026/policy-gradient/
Clean explanation of reinforce https://twitter.com/fpedregosa/status/2076337734214377492
Many users thank Fabian Pedregosa for resuming his blog series deriving REINFORCE estimators and policy gradients because it offers clear first-principles explanations of RL concepts.
Based on 6 visible X reactions from 7 accounts; directional sample.
Ask a question below.
Published answers will appear here.
Clean explanation of reinforce https://twitter.com/fpedregosa/status/2076337734214377492