Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Implement Pop-Art #6

Open
Kaixhin opened this issue Apr 13, 2016 · 0 comments
Open

Implement Pop-Art #6

Kaixhin opened this issue Apr 13, 2016 · 0 comments

Comments

@Kaixhin
Copy link
Owner

Kaixhin commented Apr 13, 2016

Learning functions across many orders of magnitudes introduces Preserving Outputs Precisely, while Adaptively Rescaling Targets (Pop-Art). In summary it normalises outputs across orders of magnitudes and gets rid of the clipping (i.e. counting) rewards heuristic for Atari games. The normalisation is also better for non-stationary problems, i.e., any decent real world problem.

The below is a picture of extra notes from the authors, next to their poster at NIPS 2016:
img_20161207_190428

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Projects
None yet
Development

No branches or pull requests

1 participant