algosrl 0.1.0

Algorithm OS RL: An RL module for AlgOS

AlgOSRL: RL Module For AlgOS

This module utilises AlgOS as its core and provides an interface for implementing reinforcement learning algorithms.

AlgOS provides standardised:

• experimental interface,
• self organising algorithmic segments via AbstractParametered and AbstractComponent,
• logging (can replace tensorboard logging with internal logger as .record interface is same),
• central DB schema that supports PostgreSQL and MySQL (integrates with Optuna DB),
• factory methods for dependency injection,
• hyperparameter for automated hyperparameter optimisation, and
• input parsing for automated script generation.

AlgOSRL extends this to work in the reinforcement learning space by:

• providing an RLExperiment and RLComponent interface to extend,
• a numpy replay buffer that offers indexed access,
• wrappers for goal based RL that work with the maze environments from robotics_gymnasium,
• wrapper for stable baselines 3 that allow the use of their off-policy agents, and
• an evaluator for goals based on their coverage.

A wrapper for Tianshou also provided but has limited testing due to its incompatibility with Dict based inputs with its replay buffer and the integration of its replay buffer.

Installation

Currently the install method is to invoke the following from the cloned directory:

pip install -e .

Documentation

TBA

Examples

The examples folder contains an example runner script (runs a single experiment) and an example optimisation script (runs a study to find optimal hyperparameters). The optimisation script will run locally by default.

Roadmap

• Provide Colab or Hugging face examples.
• Generate documentation.
• Include probabalistic curriculum learning code.

Citing the Project

To cite this repository in publications:

@misc{algos,
      title={AlgOS: Algorithm Operating System}, 
      author={Llewyn Salt and Marcus Gallagher},
      year={2025},
      eprint={2504.04909},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2504.04909}, 
}