PyDRL
A Python Deep Reinforcement Learning library. We make the whl so that you don't have to reinvent it
To Do
- Agents -
- Random Agent
- CEM Agent
- DQN
- DQN with Target Value Network
- Double DQN
- Dueling DQN
- Policy Gradient
- Actor Critic
- Advantage Actor Critic - A2C
- Asynchronous Advantage Actor Critic - A3C
- Utilities -
- Experience Replay
- Prioritised Experience Replay
- Ring Buffer
1 maintainer
Metadata
1 maintainer
Release files for PyDRL 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| PyDRL-0.0.1.tar.gz | 1.2 kB | Without provenance | Details for PyDRL-0.0.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| PyDRL-0.0.1-py3-none-any.whl | Python 3 | none | any | Details for PyDRL-0.0.1-py3-none-any.whl |
Total release size: 2.7 kB
Release files / PyDRL-0.0.1.tar.gz
| Download URL | PyDRL-0.0.1.tar.gz |
|---|---|
| Size | 1.2 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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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Release files / PyDRL-0.0.1-py3-none-any.whl
| Download URL | PyDRL-0.0.1-py3-none-any.whl |
|---|---|
| Size | 1.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Security for PyDRL 0.0.1
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