⚡ SheepRL 🐑
An easy-to-use framework for reinforcement learning in PyTorch, accelerated with Lightning Fabric.
Please have a look at the GitHub README for more information.
Contacts
You can contact us for any further questions or discussions:
- Federico Belotti: federico.belotti@orobix.com
- Davide Angioni: davide.angioni@orobix.com
- Refik Can Malli: refikcan.malli@orobix.com
- Michele Milesi: michele.milesi@orobix.com
3 maintainers
Metadata
3 maintainers
Release files for sheeprl 0.6.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sheeprl-0.6.1.tar.gz | 259.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sheeprl-0.6.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 612.7 kB
Release files / sheeprl-0.6.1.tar.gz
| Download URL | sheeprl-0.6.1.tar.gz |
|---|---|
| Size | 259.9 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Yes |
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twine/7.0.0 CPython/3.13.14
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Transparency logRelease files / sheeprl-0.6.1-py3-none-any.whl
| Download URL | sheeprl-0.6.1-py3-none-any.whl |
|---|---|
| Size | 352.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
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