Adaptable tools to make reinforcement learning and evolutionary computation algorithms.

Related tags

Deep Learningpythonresearchreinforcement-learningpytorchanvilevolutionary-computation
Overview

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Pearl

The Parallel Evolutionary and Reinforcement Learning Library (Pearl) is a pytorch based package with the goal of being excellent for rapid prototyping of new adaptive decision making algorithms in the intersection between reinforcement learning (RL) and evolutionary computation (EC). As such, this is not intended to provide template pre-built algorithms as a baseline, but rather flexible tools to allow the user to quickly build and test their own implementations and ideas. A technical report can be found here.

Main Features

Features Pearl
RL algorithms (e.g. Actor Critic) ✔️
EC algorithms (e.g. Genetic Algorithm) ✔️
Hybrid algorithms (e.g. CEM-DDPG) ✔️
Multi-agent suppport ✔️
Tensorboard integration ✔️
Modular and extensible components ✔️
Opinionated module settings ✔️
Custom callbacks ✔️

User Guide

Installation

There are two options to install this package:

  1. pip install pearll
  2. git clone [email protected]:LondonNode/Pearl.git

Module Guide

  • agents: implementations of RL and EC agents where the other modular components are put together
  • buffers: these handle storing and sampling of trajectories
  • callbacks: inject logic for every step made in an environment (e.g. save model, early stopping)
  • common: common methods applicable to all other modules (e.g. enumerations) and a main utils.py file with some useful general logic
  • explorers: action explorers for enhanced exploration by adding noise to actions and random exploration for first n steps
  • models: neural network structures which are structured as encoder -> torso -> head
  • signal_processing: signal processing logic for extra modularity (e.g. TD returns, GAE)
  • updaters: update neural networks and adaptive/iterative algorithms
  • settings.py: settings objects for the above components, can be extended for custom components

Agent Templates

See pearll/agents/templates.py for the templates to create your own agents! For more examples, see specific agent implementations under pearll/agents.

Agent Performance

To see training performance, use the command tensorboard --logdir runs or tensorboard --logdir <tensorboard_log_path> defined in your algorithm class initialization.

Python Scripts

To run these you'll need to go to wherever the library is installed, cd pearll.

  • demo.py: script to run very basic demos of agents with pre-defined hyperparameters, run python3 -m pearll.demo -h for more info
  • plot.py: script to plot more complex plots that can't be obtained via Tensorboard (e.g. multiple subplots), run python3 -m pearll.plot -h for more info

Developer Guide

Scripts

Linux

  1. scripts/setup_dev.sh: setup your virtual environment
  2. scripts/run_tests.sh: run tests

Windows

  1. scripts/windows_setup_dev.bat: setup your virtual environment
  2. scripts/windows_run_tests.bat: run tests

Dependency Management

Pearl uses poetry for dependency management and build release instead of pip. As a quick guide:

  1. Run poetry add [package] to add more package dependencies.
  2. Poetry automatically handles the virtual environment used, check pyproject.toml for specifics on the virtual environment setup.
  3. If you want to run something in the poetry virtual environment, add poetry run as a prefix to the command you want to execute. For example, to run a python file: poetry run python3 script.py.

Credit

Citing Pearl

@misc{tangri2022pearl,
      title={Pearl: Parallel Evolutionary and Reinforcement Learning Library}, 
      author={Rohan Tangri and Danilo P. Mandic and Anthony G. Constantinides},
      year={2022},
      eprint={2201.09568},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Acknowledgements

Pearl was inspired by Stable Baselines 3 and Tonic

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Comments
  • Bump pillow from 9.0.0 to 9.0.1

    Bump pillow from 9.0.0 to 9.0.1

    Bumps pillow from 9.0.0 to 9.0.1.

    Release notes

    Sourced from pillow's releases.

    9.0.1

    https://pillow.readthedocs.io/en/stable/releasenotes/9.0.1.html

    Changes

    • In show_file, use os.remove to remove temporary images. CVE-2022-24303 #6010 [@​radarhere, @​hugovk]
    • Restrict builtins within lambdas for ImageMath.eval. CVE-2022-22817 #6009 [radarhere]
    Changelog

    Sourced from pillow's changelog.

    9.0.1 (2022-02-03)

    • In show_file, use os.remove to remove temporary images. CVE-2022-24303 #6010 [radarhere, hugovk]

    • Restrict builtins within lambdas for ImageMath.eval. CVE-2022-22817 #6009 [radarhere]

    Commits
    • 6deac9e 9.0.1 version bump
    • c04d812 Update CHANGES.rst [ci skip]
    • 4fabec3 Added release notes for 9.0.1
    • 02affaa Added delay after opening image with xdg-open
    • ca0b585 Updated formatting
    • 427221e In show_file, use os.remove to remove temporary images
    • c930be0 Restrict builtins within lambdas for ImageMath.eval
    • 75b69dd Dont need to pin for GHA
    • cd938a7 Autolink CWE numbers with sphinx-issues
    • 2e9c461 Add CVE IDs
    • See full diff in compare view

    Dependabot compatibility score

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    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 1
  • Feature/hybrid

    Feature/hybrid

    Overhaul models and base agent structure to accommodate RL, MARL, EC in optimizing static functions and RL environments and hybrid algorithms combining RL and EC.

    opened by 09tangriro 1
  • MORE AGENTS

    MORE AGENTS

    The more agents created the better proof that the tools underlying work as intended.

    Agents should be tested on particular environments to ensure performance.

    feature good first issue 
    opened by 09tangriro 0
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