reinforced-lib 1.1.2

Description:

reinforcedlib 1.1.2

Reinforced-lib: Reinforcement learning library





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,
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:
pip install reinforced-lib

To have easy access to the example files
you can clone the source code from our repository, and than install it locally with pip:
git clone [email protected]:m-wojnar/reinforced-lib.git
cd reinforced-lib
pip install .

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:
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.

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 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.
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:
@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}
}