RLtime is a reinforcement learning library, currently supporting PyTorch, with focus on state-of-the-art q-learning algorithms and features, and interacting with real-time environments which require low-latency acting and sample-efficient training. The latest code and instructions can be found here: https://github.com/opherlieber/rltime
Metadata
Release files for rltime 0.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| rltime-0.1.2.tar.gz | 86.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| rltime-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 219.3 kB
Release files / rltime-0.1.2.tar.gz
| Download URL | rltime-0.1.2.tar.gz |
|---|---|
| Size | 86.7 kB |
| Tags | Source |
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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/1.14.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.35.0 CPython/3.6.5
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Release files / rltime-0.1.2-py3-none-any.whl
| Download URL | rltime-0.1.2-py3-none-any.whl |
|---|---|
| Size | 132.6 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? |
No |
| Uploaded via |
twine/1.14.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.35.0 CPython/3.6.5
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