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The Little Book of Reinforcement Learning

github.com

213 points by mustaphah 22 days ago · 28 comments

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verdverm 22 days ago

This looks like a good pre-read for Nathan Lambert's https://rlhfbook.com/

newsomix9xl 22 days ago

Real biological operant behavior isn't exactly trial and error learning.

Many factors shape and guide initial responses.

What I've noticed in some descriptions of models is the use of optimization for reinforcement to shape responses. In real organisms behavior may be controlled by short or long term outcomes, and may oscillate between this "optimization" based on schedules. This produces variability in the trials which can adjust behavior. Are we seeing these reinforcement models do this?

  • herodoturtle 22 days ago

    I found this comment/question deeply intriguing.

    I’m no expert at this and was wondering what you meant by the following:

    > In real organisms behavior may be controlled by short or long term outcomes, and may oscillate between this "optimization" based on schedules

    Could you perhaps provide an example that would help me understand what you mean?

    Thanks for the insightful comment either way.

    • newsomix9xl 21 days ago

      In humans this is often called impulsivity, the preference for smaller sooner outcomes. This is often seen in children and animals and in some adult human behavior.

      An impulsive choice is not optimal. You can buy a cheaper pack of gum at Costco in a week or get one for three times the cost right now.

  • ainch 21 days ago

    There is a field of hierarchical RL in which the optimisation occurs over a range of time scales/abstraction. But I'm not aware of much practical success for these approaches so far.

programjames 22 days ago

I skimmed through the book, and it's lacking the information theory foundations. For example, "trust region methods" come from maximizing the policy's relative entropy (to a reference policy) under a tournament system where high-scoring agents are exponentially likely to survive. In general, a reward is the negative bits it costs an environment to propagate an agent (multiplied by some temperature).

  • ainch 21 days ago

    Do you have a good source on this information theory framing? I don't remember it being covered in Sutton & Barto.

    • porridgeraisin 21 days ago

      It's just another way to frame it. It's as foundational as the many other ways to frame it. I'm not aware of any major insight you get specifically from this framing. Is there one?

janalsncm 22 days ago

I wonder what Sutton thinks about some of the more recent innovations in RL like GRPO. In some ways it’s new, in other ways it’s an echo of RLOO.

  • porridgeraisin 21 days ago

    GRPO is policy gradient/PPO with your value function baseline monte carlo estimated using k rollouts. The only new thing is finding out it works well with binary rewards and LLM policies.

    • janalsncm 21 days ago

      It is a huge improvement to PPO because you don’t need a separate critic model which cuts memory costs in half and stabilizes training.

laurensr 21 days ago

This reminds me of the Little Book of Calm, discussed extensively in the Black Books TV series.

johnea 22 days ago

Is this riffing on Strunk and Whites: The Elements of Style?

Often referred to as "The Little Book".

Envwnger 21 days ago

Should have named it little RL book.

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