PyRL
Environment Agnostic RL algorithm implementations using Pytorch. High quality code, typehints, thorough tests, examples. Also uses minibatches correctly, which most public libraries don't implement.
See examples for some, well, examples. Algos implemented:
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Deep Q Learning (DQN) (Mnih et al. 2013)
--- UPCOMING --- -
DQN Experience Replay (Mnih et al. 2013)
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DQN with Fixed targets (Mnih et al. 2013)
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Double Q Learning (DDQN) (arXiv:1509.06461v3 [cs.LG] 8 Dec 2015)
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REINFORCE (Richard S. Sutton et al 1999)
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Advantage Actor Critic (arXiv:1611.06256)
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PPO
What i'm happy with Quality of the code, thorough tests, majority of functionality, ease of use & versatility
Run tests with: pytest tests
Release files for rldog 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| rldog-0.1.0.tar.gz | 17.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| rldog-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 44.2 kB
Release files / rldog-0.1.0.tar.gz
| Download URL | rldog-0.1.0.tar.gz |
|---|---|
| Size | 17.5 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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poetry/1.3.1 CPython/3.10.5 Windows/10
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Release files / rldog-0.1.0-py3-none-any.whl
| Download URL | rldog-0.1.0-py3-none-any.whl |
|---|---|
| Size | 26.7 kB |
| Tags | Python 3 |
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poetry/1.3.1 CPython/3.10.5 Windows/10
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