ToyRL
Documentation
https://ai-glimpse.github.io/toyrl
Installation
pip install toyrl
Algorithms
- REINFORCE
- SARSA
- DQN & Double DQN
- A2C
- PPO
References
- kengz/SLM-Lab: Our implementations are inspired by the book "Foundations of Deep Reinforcement Learning" and the implementation of SLM-Lab.
- vwxyzjn/cleanrl: The main reference for the implementation of the PPO implementation.
1 maintainer
Metadata
1 maintainer
Release files for toyrl 0.3.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| toyrl-0.3.1.tar.gz | 20.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| toyrl-0.3.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 41.5 kB
Release files / toyrl-0.3.1.tar.gz
| Download URL | toyrl-0.3.1.tar.gz |
|---|---|
| Size | 20.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
e179ff649e513f00fbf75fa38bc23fd05e4940b302b30e6c474085d3d561d0a1
|
|
BLAKE2b-256 checksum How to use checksums |
2ca62db5eafc0e8e38f9646ef375991d12884c3f06780288dd5a3c6cf79bec03
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
uv/0.7.3
|
Release files / toyrl-0.3.1-py3-none-any.whl
| Download URL | toyrl-0.3.1-py3-none-any.whl |
|---|---|
| Size | 20.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
62f61e6323c8cc02d3fd5c3e3632fd55fc7df3a9a45988ecb84cb320e28c6687
|
|
BLAKE2b-256 checksum How to use checksums |
5923c7bee07cefba6672fab38422d4e6f37ea003c197cac49edc66816f324795
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
uv/0.7.3
|
