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Python library for Reinforcement Learning.
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Momentum
0.0
growth rate 0.00%/day
**********
MushroomRL
**********
.. image:: https://github.com/MushroomRL/mushroom-rl/actions/workflows/continuous_integration.yml/badge.svg?branch=dev
:target: https://github.com/MushroomRL/mushroom-rl/actions/workflows/continuous_integration.yml
:alt: Continuous Integration
.. image:: https://app.readthedocs.org/projects/mushroomrl/badge/?version=dev
:target: https://mushroomrl.readthedocs.io/en/dev/?badge=dev
:alt: Documentation Status
.. image:: https://qlty.sh/gh/MushroomRL/projects/mushroom-rl/maintainability.svg
:target: https://qlty.sh/gh/MushroomRL/projects/mushroom-rl
:alt: Maintainability
.. image:: https://qlty.sh/gh/MushroomRL/projects/mushroom-rl/coverage.svg
:target: https://qlty.sh/gh/MushroomRL/projects/mushroom-rl
:alt: Test Coverage
**MushroomRL: Reinforcement Learning Python library.**
.. contents:: **Contents of this document:**
:depth: 2
What is MushroomRL
==================
MushroomRL is a Python Reinforcement Learning (RL) library whose modularity allows
to easily use well-known Python libraries for tensor computation (e.g. PyTorch,
Tensorflow) and RL benchmarks (e.g. Gymnasium, PyBullet, Deepmind Control Suite).
It allows to perform RL experiments in a simple way providing classical RL algorithms
(e.g. Q-Learning, SARSA, FQI), and deep RL algorithms (e.g. DQN, DDPG, SAC, TD3,
TRPO, PPO).
`Full documentation and tutorials available here <http://mushroomrl.readthedocs.io/en/latest/>`_.
Installation
============
You can do a minimal installation of ``MushroomRL`` with:
.. code:: shell
pip install mushroom-rl
Installing everything
---------------------
``MushroomRL`` contains also some optional components e.g., support for ``Gymnasium``
environments, Atari 2600 games from the ``Arcade Learning Environment``, and the support
for physics simulators such as ``Pybullet`` and ``MuJoCo``.
Support for these classes is not enabled by default.
To install the whole set of features, you will need additional packages installed.
You can install everything by running:
.. code:: shell
pip install mushroom-rl[all]
This will install every dependency of MushroomRL, except Box2D, PyBullet and Isaac Sim.
For ubuntu>20.04, you may need to install pygame and gym dependencies:
.. code:: shell
sudo apt -y install libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev \
libsdl1.2-dev libsmpeg-dev libportmidi-dev ffmpeg libswscale-dev \
libavformat-dev libavcodec-dev swig
Notice that you still need to install some of these dependencies for different operating systems, e.g. swig for macOS
Below is the code that you need to run to install the live monitors dependencies:
.. code:: shell
pip install mushroom-rl[monitors]
Isaac Sim
---------
Support for ``NVIDIA Isaac Sim`` environments is provided by a separate extra, which is deliberately
not part of ``[all]``:
.. code:: shell
pip install mushroom-rl[isaacsim]
Install it in a dedicated environment, never alongside the other extras:
* ``Isaac Sim`` supports **Python 3.12 only** (its metadata declares ``Requires-Python: ==3.12.*``),
whereas ``MushroomRL`` also supports Python 3.11.
* ``isaacsim-core`` pins exact versions of packages shared with the other extras (``mujoco==3.11.0``,
``mujoco-warp==3.11.0``, ``torch==2.11.0``, ``scipy==1.17.0``), so they cannot move past those versions in
the same environment.
Running the ``Isaac Sim`` environments additionally requires an NVIDIA RTX GPU. They are therefore not
covered by continuous integration, and their tests are skipped unless ``isaacsim`` can be imported.
Editable Installation
---------------------
You can also perform a local editable installation by using:
.. code:: shell
pip install -e .
To install also optional dependencies:
.. code:: shell
pip install -e .[all]
How to set and run and experiment
=================================
To run experiments, MushroomRL requires a script file thatExcerpt of 5,037 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6589cbec951936df, topic:reinforcement-learning, topic:deep-reinforcement-learning, topic:rl