| Name | reinforced-lib JSON |
| Version |
1.2.3
JSON |
| download |
| home_page | None |
| Summary | Reinforcement learning library |
| upload_time | 2025-07-30 10:45:52 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <4,>=3.12 |
| license | Attribution 4.0 International
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|
| keywords |
deep-reinforcement-learning
jax
library
machine-learning
reinforcement-learning
|
| VCS |
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No requirements were recorded.
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|
# Reinforced-lib: Reinforcement learning library
[![PyPI version][pypi-badge]][pypi]
[![License: CC-BY 4.0][license-badge]][license]
[![build and test][tests-badge]][github-actions]
[![Documentation Status][rtd-badge]][documentation]
[](https://doi.org/10.1016/j.softx.2024.101706)
[pypi-badge]: https://img.shields.io/pypi/v/reinforced-lib
[pypi]: https://pypi.org/project/reinforced-lib/
[license-badge]: https://img.shields.io/badge/Licenses-CC_BY_4.0-brightgreen
[license]: https://github.com/m-wojnar/reinforced-lib/blob/main/LICENSE
[tests-badge]: https://github.com/m-wojnar/reinforced-lib/actions/workflows/python-package.yml/badge.svg
[github-actions]: https://github.com/m-wojnar/reinforced-lib/actions
[rtd-badge]: https://readthedocs.org/projects/reinforced-lib/badge/?version=latest
[documentation]: https://reinforced-lib.readthedocs.io/en/latest/
**Introducing Reinforced-lib:** a lightweight Python library for the rapid development of reinforcement-learning (RL)
solutions. It is open-source, prioritizes ease of use, provides comprehensive documentation, and offers both deep
reinforcement learning (DRL) and classic non-neural agents. Built on [JAX](https://jax.readthedocs.io/en/latest/),
it facilitates exporting trained models to embedded devices, and makes it great for research and prototyping with RL
algorithms. Access to JAX's just-in-time (JIT) compilation ensures high-performance results.
## Installation
You can install the latest version of Reinforced-lib from PyPI:
```bash
pip install reinforced-lib
```
To have easy access to the [example files](https://github.com/m-wojnar/reinforced-lib/tree/main/examples)
you can clone the source code from our repository, and then install it locally with pip:
```bash
git clone git@github.com:m-wojnar/reinforced-lib.git
cd reinforced-lib
pip install -e .
```
In the spirit of making Reinforced-lib a lightweight solution, we include only the necessary dependencies in the base
requirements. To fully benefit from Reinforced-lib's conveniences, such as TF Lite export, install with the "full" suffix:
```bash
pip install ".[full]"
```
## Key components
Reinforced-lib facilitates seamless interaction between RL agents and the environment. Here are the key components
within of the library, represented in the API as different modules.
- **RLib** - The core module which provides a simple and intuitive interface to manage agents, use extensions,
and configure the logging system. Even if you're not an RL expert, *RLib* makes it easy to implement the
agent-environment interaction loop.
- **Agents** - Choose from a variety of RL agents available in the *Agents* module. These agents are designed to be
versatile and work with any environment. If needed, you can even create your own agents using our documented recipes.
- **Extensions** - Enhance agent observations with domain-specific knowledge by adding a suitable extension from the
*Extensions* module. This module enables seamless agent switching and parameter tuning without extensive reconfiguration.
- **Loggers** - This module allows you to monitor agent-environment interactions. Customize and adapt logging to your
specific needs, capturing training metrics, internal agent state, or environment observations. The library includes
various loggers for creating plots and output files, simplifying visualization and data processing.
The figure below provides a visual representation of Reinforced-lib and the data-flow between its modules.
<img src="docs/resources/data-flow.png" width="600">
## JAX Backend
Our library is built on top of JAX, a high-performance numerical computing library. JAX makes it easy to implement
RL algorithms efficiently. It provides powerful transformations, including JIT compilation, automatic differentiation,
vectorization, and parallelization. Our library is fully compatible with DeepMind's JAX ecosystem, granting access to
state-of-the-art RL models and helper libraries. JIT compilation significantly accelerates execution and ensures
portability across different architectures (CPUs, GPUs, TPUs) without requiring code modifications.
JAX offers another benefit through its robust pseudorandom number generator system, employed in our library to
guarantee result reproducibility. This critical aspect of scientific research is frequently underestimated but
remains highly significant.
## Edge Device Export
Reinforced-lib is designed to work seamlessly on wireless, low-powered devices, where resources are limited. It's the
perfect solution for energy-constrained environments that may struggle with other ML frameworks. You can export your
trained models to [TensorFlow Lite](https://www.tensorflow.org/lite) with ease, reducing runtime overhead and
optimizing performance. This means you can deploy RL agents on resource-limited devices efficiently.
## Example code
Experience the simplicity of our library and witness the fundamental agent-environment interaction loop with our
straightforward example. This code can by used to train a deep Q-learning agent on the `CartPole-v1` environment
effortlessly using Reinforced-lib.
```python
import gymnasium as gym
import optax
from chex import Array
from flax import linen as nn
from reinforced_lib import RLib
from reinforced_lib.agents.deep import DQN
from reinforced_lib.exts import Gymnasium
class QNetwork(nn.Module):
@nn.compact
def __call__(self, x: Array) -> Array:
x = nn.Dense(256)(x)
x = nn.relu(x)
return nn.Dense(2)(x)
if __name__ == '__main__':
rl = RLib(
agent_type=DQN,
agent_params={
'q_network': QNetwork(),
'optimizer': optax.rmsprop(3e-4, decay=0.95, eps=1e-2),
},
ext_type=Gymnasium,
ext_params={'env_id': 'CartPole-v1'}
)
for epoch in range(300):
env = gym.make('CartPole-v1', render_mode='human')
_, _ = env.reset()
action = env.action_space.sample()
terminal = False
while not terminal:
env_state = env.step(action.item())
action = rl.sample(*env_state)
terminal = env_state[2] or env_state[3]
```
## Citing Reinforced-lib
To cite this repository, please use the following BibTeX entry for the Reinforced-lib paper:
```bibtex
@article{reinforcedlib2022,
author = {Maksymilian Wojnar and Szymon Szott and Krzysztof Rusek and Wojciech Ciezobka},
title = {{R}einforced-lib: {R}apid prototyping of reinforcement learning solutions},
journal = {SoftwareX},
volume = {26},
pages = {101706},
year = {2024},
issn = {2352-7110},
doi = {https://doi.org/10.1016/j.softx.2024.101706},
url = {https://www.sciencedirect.com/science/article/pii/S2352711024000773}
}
```
Raw data
{
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"home_page": null,
"name": "reinforced-lib",
"maintainer": null,
"docs_url": null,
"requires_python": "<4,>=3.12",
"maintainer_email": null,
"keywords": "deep-reinforcement-learning, jax, library, machine-learning, reinforcement-learning",
"author": null,
"author_email": "Maksymilian Wojnar <mwojnar@agh.edu.pl>, Wojciech Ci\u0119\u017cobka <wciezobka@agh.edu.pl>, Krzysztof Rusek <krusek@agh.edu.pl>",
"download_url": "https://files.pythonhosted.org/packages/ed/75/51587ff55016343b353a15d00e0d194b90dcd9bb0442f0a7dd84d1819aa3/reinforced_lib-1.2.3.tar.gz",
"platform": null,
"description": "# Reinforced-lib: Reinforcement learning library\n\n[![PyPI version][pypi-badge]][pypi]\n[![License: CC-BY 4.0][license-badge]][license]\n[![build and test][tests-badge]][github-actions]\n[![Documentation Status][rtd-badge]][documentation]\n[](https://doi.org/10.1016/j.softx.2024.101706)\n\n[pypi-badge]: https://img.shields.io/pypi/v/reinforced-lib\n[pypi]: https://pypi.org/project/reinforced-lib/\n[license-badge]: https://img.shields.io/badge/Licenses-CC_BY_4.0-brightgreen\n[license]: https://github.com/m-wojnar/reinforced-lib/blob/main/LICENSE\n[tests-badge]: https://github.com/m-wojnar/reinforced-lib/actions/workflows/python-package.yml/badge.svg\n[github-actions]: https://github.com/m-wojnar/reinforced-lib/actions\n[rtd-badge]: https://readthedocs.org/projects/reinforced-lib/badge/?version=latest\n[documentation]: https://reinforced-lib.readthedocs.io/en/latest/\n\n**Introducing Reinforced-lib:** a lightweight Python library for the rapid development of reinforcement-learning (RL) \nsolutions. It is open-source, prioritizes ease of use, provides comprehensive documentation, and offers both deep \nreinforcement learning (DRL) and classic non-neural agents. Built on [JAX](https://jax.readthedocs.io/en/latest/), \nit facilitates exporting trained models to embedded devices, and makes it great for research and prototyping with RL \nalgorithms. Access to JAX's just-in-time (JIT) compilation ensures high-performance results.\n\n## Installation\n\nYou can install the latest version of Reinforced-lib from PyPI:\n\n```bash\npip install reinforced-lib\n```\n\nTo have easy access to the [example files](https://github.com/m-wojnar/reinforced-lib/tree/main/examples)\nyou can clone the source code from our repository, and then install it locally with pip:\n\n```bash\ngit clone git@github.com:m-wojnar/reinforced-lib.git\ncd reinforced-lib\npip install -e .\n```\n\nIn the spirit of making Reinforced-lib a lightweight solution, we include only the necessary dependencies in the base \nrequirements. To fully benefit from Reinforced-lib's conveniences, such as TF Lite export, install with the \"full\" suffix:\n\n```bash\npip install \".[full]\"\n```\n\n## Key components\n\nReinforced-lib facilitates seamless interaction between RL agents and the environment. Here are the key components \nwithin of the library, represented in the API as different modules.\n\n- **RLib** - The core module which provides a simple and intuitive interface to manage agents, use extensions, \n and configure the logging system. Even if you're not an RL expert, *RLib* makes it easy to implement the \n agent-environment interaction loop.\n\n- **Agents** - Choose from a variety of RL agents available in the *Agents* module. These agents are designed to be \n versatile and work with any environment. If needed, you can even create your own agents using our documented recipes.\n\n- **Extensions** - Enhance agent observations with domain-specific knowledge by adding a suitable extension from the \n *Extensions* module. This module enables seamless agent switching and parameter tuning without extensive reconfiguration.\n\n- **Loggers** - This module allows you to monitor agent-environment interactions. Customize and adapt logging to your \n specific needs, capturing training metrics, internal agent state, or environment observations. The library includes \n various loggers for creating plots and output files, simplifying visualization and data processing.\n\nThe figure below provides a visual representation of Reinforced-lib and the data-flow between its modules.\n\n<img src=\"docs/resources/data-flow.png\" width=\"600\">\n\n## JAX Backend\n\nOur library is built on top of JAX, a high-performance numerical computing library. JAX makes it easy to implement \nRL algorithms efficiently. It provides powerful transformations, including JIT compilation, automatic differentiation, \nvectorization, and parallelization. Our library is fully compatible with DeepMind's JAX ecosystem, granting access to \nstate-of-the-art RL models and helper libraries. JIT compilation significantly accelerates execution and ensures \nportability across different architectures (CPUs, GPUs, TPUs) without requiring code modifications. \nJAX offers another benefit through its robust pseudorandom number generator system, employed in our library to \nguarantee result reproducibility. This critical aspect of scientific research is frequently underestimated but \nremains highly significant.\n\n## Edge Device Export\n\nReinforced-lib is designed to work seamlessly on wireless, low-powered devices, where resources are limited. It's the \nperfect solution for energy-constrained environments that may struggle with other ML frameworks. You can export your \ntrained models to [TensorFlow Lite](https://www.tensorflow.org/lite) with ease, reducing runtime overhead and \noptimizing performance. This means you can deploy RL agents on resource-limited devices efficiently.\n\n## Example code\n\nExperience the simplicity of our library and witness the fundamental agent-environment interaction loop with our \nstraightforward example. This code can by used to train a deep Q-learning agent on the `CartPole-v1` environment \neffortlessly using Reinforced-lib.\n\n```python\nimport gymnasium as gym\nimport optax\nfrom chex import Array\nfrom flax import linen as nn\n\nfrom reinforced_lib import RLib\nfrom reinforced_lib.agents.deep import DQN\nfrom reinforced_lib.exts import Gymnasium\n\n\nclass QNetwork(nn.Module):\n @nn.compact\n def __call__(self, x: Array) -> Array:\n x = nn.Dense(256)(x)\n x = nn.relu(x)\n return nn.Dense(2)(x)\n\n\nif __name__ == '__main__':\n rl = RLib(\n agent_type=DQN,\n agent_params={\n 'q_network': QNetwork(),\n 'optimizer': optax.rmsprop(3e-4, decay=0.95, eps=1e-2),\n },\n ext_type=Gymnasium,\n ext_params={'env_id': 'CartPole-v1'}\n )\n\n for epoch in range(300):\n env = gym.make('CartPole-v1', render_mode='human')\n\n _, _ = env.reset()\n action = env.action_space.sample()\n terminal = False\n\n while not terminal:\n env_state = env.step(action.item())\n action = rl.sample(*env_state)\n terminal = env_state[2] or env_state[3]\n```\n\n## Citing Reinforced-lib\n\nTo cite this repository, please use the following BibTeX entry for the Reinforced-lib paper:\n\n```bibtex\n@article{reinforcedlib2022,\n author = {Maksymilian Wojnar and Szymon Szott and Krzysztof Rusek and Wojciech Ciezobka},\n title = {{R}einforced-lib: {R}apid prototyping of reinforcement learning solutions},\n journal = {SoftwareX},\n volume = {26},\n pages = {101706},\n year = {2024},\n issn = {2352-7110},\n doi = {https://doi.org/10.1016/j.softx.2024.101706},\n url = {https://www.sciencedirect.com/science/article/pii/S2352711024000773}\n}\n```\n",
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