dm_control: DeepMind Infrastructure for Physics-Based Simulation.
DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo physics.
An introductory tutorial for this package is available as a Colaboratory notebook: Open In Google Colab.
Metadata
Release files for dm-control 1.0.50
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| dm_control-1.0.50.tar.gz | 56.3 MB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| dm_control-1.0.50-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 112.7 MB
Release files / dm_control-1.0.50.tar.gz
| Download URL | dm_control-1.0.50.tar.gz |
|---|---|
| Size | 56.3 MB |
| Tags | Source |
|
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? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.13.15
|
Release files / dm_control-1.0.50-py3-none-any.whl
| Download URL | dm_control-1.0.50-py3-none-any.whl |
|---|---|
| Size | 56.5 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
5434e1e51ff69016867426da7df6064b18d140e4d0a628f879bbcf0e7d0a586d
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|
BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.13.15
|
Security for dm-control 1.0.50
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