google-deepmind / google-deepmind/scalable_agent
Verify evals on Papers with Code
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- Python
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Description
Hi,
Niels here from the open-source team at Hugging Face. Congratulations on your work!
I've made the [paper](https://paperswithcode.co/paper/1802.01561) and [1 paper-native evaluation](https://paperswithcode.co/paper/1802.01561#results) available on Papers with Code.
The paper is part of the [Atari Games](https://paperswithcode.co/tasks/atari-games) task page.
The IMPALA (deep, experts) result currently ranks second on [Atari 57](https://paperswithcode.co/benchmark/atari-57-200m-noop?task=atari-games&eval=13479).
Would it be possible to verify these results and let me know if any score, model name, benchmark protocol, or openness metadata should be corrected?
You can also edit the task, methods, project page, and GitHub URL directly from the paper page using your Hugging Face account.
If you'd like to showcase the results in your repository README, you can copy these live leaderboard badges (or use the “Copy PwC badge” button in the Results section):
[](https://paperswithcode.co/api/v1/papers/1802.01561/leaderboard-badge-link?eval=13479)
Kind regards,
Niels
Contributor guide
Research direction
Start with the linked paper and its Papers with Code Atari 57 result, then compare the listed score, model name, benchmark protocol, and openness metadata with the repository and paper. Done means reporting any corrections or confirming that the published evaluation details are accurate; the issue does not name a repository file or test to run.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Active
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100