Machine Learning

1 readers

1 users here now

Community Rules:

Be nice. No offensive behavior, insults or attacks: we encourage a diverse community in which members feel safe and have a voice.
Make your post clear and comprehensive: posts that lack insight or effort will be removed. (ex: questions which are easily googled)
Beginner or career related questions go elsewhere. This community is focused in discussion of research and new projects that advance the state-of-the-art.
Limit self-promotion. Comments and posts should be first and foremost about topics of interest to ML observers and practitioners. Limited self-promotion is tolerated, but the sub is not here as merely a source for free advertisement. Such posts will be removed at the discretion of the mods.

founded 1 year ago

MODERATORS

communick@academy.garden

[R] Rethinking Open'sAI's Q-Learning : Insights from the Award-Winning 'Non-delusional Q-learning' Paper (alien.top)

submitted 11 months ago by Even_Campaign7385@alien.top to c/machinelearning@academy.garden

8 comments fedilink hide all child comments

OpenAI's approach to Q-Learning has been drawing significant attention recently.

However, there's a fundamental issue in the way Q-learning is typically implemented in deep learning and neural network environments. This concern is highlighted in the award-winning paper "Non-delusional Q-learning," presented at NeurIPS.

The paper suggests a fundamental flaw in the blind application of Q-learning updates to deep neural networks. It points out that such updates can create a self-contradictory scenario where improving the network for the current batch of data inadvertently makes it less effective for other batches. This is akin to a situation in supervised learning where optimizing a network for a specific set of data may degrade its performance on other datasets.

For more insights, the full paper can be accessed here: Non-delusional Q-learning Paper(Follow up ICML paper: Practical Non-delusional-Q Learning )

I'm curious about others' views on this topic. What do you think about the implications of these findings for the future of Q-learning in deep learning environments?

you are viewing a single comment's thread
view the rest of the comments

[–] residentmouse@alien.top 1 points 11 months ago

Yeah, so largely I think you’ve hit the nail but just in case you don’t know the fervour is a deliberately leaked project name “Q*” and the suggestion it precipitated the OpenAI board drama. Now, is this probably a tactic to keep prices high so stock sells @ the 65B valuation OAI had prior to the drama? Sure.

But it’s still fun to speculate.