Package: reinforced-lib
Reinforced-lib: Reinforcement learning libraryIntroducing Reinforced-lib:a lightweight Python library for the rapid development of 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 onJAX, 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.InstallationYou can install the latest version of Reinforced-lib from PyPI:pipinstallreinforced-libTo have easy access to theexample filesyou can clone the source code from our repository, and than install it locally with pip:gitclonegit@github.com:m-wojnar/reinforced-lib.gitcdreinforced-lib
pipinstall.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:pip3install".[full]"Key componentsReinforced-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 a reinforcement learning (RL) expert,RLibmakes it easy to
implement the agent-environment interaction loop.Agents- Choose from a variety of RL agents available in theAgentsmodule. 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 theExtensionsmodule. This module enables seamless agent switching and parameter tuning without extensive reconfiguration.Logging- 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 BackendOur 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.Edge Device ExportReinforced-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 toTensorFlow Litewith ease, reducing runtime overhead and
optimizing performance. This means you can deploy RL agents on resource-limited devices efficiently.Example codeExperience 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 theCartPole-v1environment
effortlessly using Reinforced-lib.importgymnasiumasgymimportoptaxfromcheximportArrayfromflaximportlinenasnnfromreinforced_libimportRLibfromreinforced_lib.agents.deepimportDQNfromreinforced_lib.extsimportGymnasiumclassQNetwork(nn.Module):@nn.compactdef__call__(self,x:Array)->Array:x=nn.Dense(256)(x)x=nn.relu(x)returnnn.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'})forepochinrange(300):env=gym.make('CartPole-v1',render_mode='human')_,_=env.reset()action=env.action_space.sample()terminal=Falsewhilenotterminal:env_state=env.step(action.item())action=rl.sample(*env_state)terminal=env_state[2]orenv_state[3]Citing Reinforced-libTo cite this repository:@software{reinforcedlib2022,
  author = {Maksymilian Wojnar and Wojciech Ciężobka},
  title = {{R}einforced-lib: {R}einforcement learning library},
  url = {http://github.com/m-wojnar/reinforced-lib},
  year = {2022},
}