Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/0xangelo/raylab

Reinforcement learning algorithms in RLlib
https://github.com/0xangelo/raylab

bokeh deep-learning generative-models machine-learning model-based-rl normalizing-flows pytorch reinforcement-learning rllib streamlit

Last synced: about 2 months ago
JSON representation

Reinforcement learning algorithms in RLlib

Host: GitHub
URL: https://github.com/0xangelo/raylab
Owner: 0xangelo
License: mit
Created: 2019-09-12T13:50:53.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2024-05-03T20:24:54.000Z (8 months ago)
Last Synced: 2024-11-08T11:59:26.805Z (about 2 months ago)
Topics: bokeh, deep-learning, generative-models, machine-learning, model-based-rl, normalizing-flows, pytorch, reinforcement-learning, rllib, streamlit
Language: Python
Size: 4.52 MB
Stars: 56
Watchers: 4
Forks: 10
Open Issues: 13
Metadata Files:
- Readme: README.rst
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Authors: AUTHORS.rst

Awesome Lists containing this project

README

======
raylab
======

.. |PyPI| image:: https://img.shields.io/pypi/v/raylab?logo=PyPi&logoColor=white&color=blue
:alt: PyPI

.. |Tests| image:: https://img.shields.io/github/workflow/status/angelolovatto/raylab/Poetry%20package?label=tests&logo=GitHub
:alt: GitHub Workflow Status

.. |Dependabot| image:: https://api.dependabot.com/badges/status?host=github&repo=angelolovatto/raylab
:target: https://dependabot.com

.. |License| image:: https://img.shields.io/github/license/angelolovatto/raylab?color=blueviolet&logo=github
:alt: GitHub

.. |CodeStyle| image:: https://img.shields.io/badge/code%20style-black-000000.svg
:target: https://github.com/psf/black

Reinforcement learning algorithms in `RLlib `_
and `PyTorch `_.

Installation
------------

.. code:: bash

pip install raylab

Quickstart
----------

Raylab provides agents and environments to be used with a normal RLlib/Tune setup.
You can an agent's name (from the `Algorithms`_ section) to :code:`raylab info list` to list its top-level configurations:

.. code-block:: zsh

raylab info list SoftAC

.. code-block::

learning_starts: 0
Hold this number of timesteps before first training operation.
policy: {}
Sub-configurations for the policy class.
wandb: {}
Configs for integration with Weights & Biases.

Accepts arbitrary keyword arguments to pass to `wandb.init`.
The defaults for `wandb.init` are:
* name: `_name` property of the trainer.
* config: full `config` attribute of the trainer
* config_exclude_keys: `wandb` and `callbacks` configs
* reinit: True

Don't forget to:
* install `wandb` via pip
* login to W&B with the appropriate API key for your
team/project.
* set the `wandb/project` name in the config dict

Check out the Quickstart for more information:
`https://docs.wandb.com/quickstart`

You can add the :code:`--rllib` flag to get the descriptions for all the options common to RLlib agents
(or :code:`Trainer`\s)

Launching experiments can be done via the command line using :code:`raylab experiment` passing a file path
with an agent's configuration through the :code:`--config` flag.
The following command uses the cartpole `example `_ configuration file
to launch an experiment using the vanilla Policy Gradient agent from the RLlib library.

.. code-block:: zsh

raylab experiment PG --name PG -s training_iteration 10 --config examples/PG/cartpole_defaults.py

You can also launch an experiment from a Python script normally using Ray and Tune.
The following shows how you may use Raylab to perform an experiment comparing different
types of exploration for the NAF agent.

.. code-block:: python

import ray
from ray import tune
import raylab

def main():
raylab.register_all_agents()
raylab.register_all_environments()
ray.init()
tune.run(
"NAF",
local_dir="data/NAF",
stop={"timesteps_total": 100000},
config={
"env": "CartPoleSwingUp-v0",
"exploration_config": {
"type": tune.grid_search([
"raylab.utils.exploration.GaussianNoise",
"raylab.utils.exploration.ParameterNoise"
])
}
},
num_samples=10,
)

if __name__ == "__main__":
main()

One can then visualize the results using :code:`raylab dashboard`, passing the :code:`local_dir` used in the
experiment. The dashboard lets you filter and group results in a quick way.

.. code-block:: zsh

raylab dashboard data/NAF/

.. image:: https://i.imgur.com/bVc6WC5.png
:align: center

You can find the best checkpoint according to a metric (:code:`episode_reward_mean` by default)
using :code:`raylab find-best`.

.. code-block:: zsh

raylab find-best data/NAF/

Finally, you can pass a checkpoint to :code:`raylab rollout` to see the returns collected by the agent and
render it if the environment supports a visual :code:`render()` method. For example, you
can use the output of the :code:`find-best` command to see the best agent in action.

.. code-block:: zsh

raylab rollout $(raylab find-best data/NAF/) --agent NAF

Algorithms
----------

+--------------------------------------------------------+-------------------------+
| Paper | Agent Name |
+--------------------------------------------------------+-------------------------+
| `Actor Critic using Kronecker-factored Trust Region`_ | ACKTR |
+--------------------------------------------------------+-------------------------+
| `Trust Region Policy Optimization`_ | TRPO |
+--------------------------------------------------------+-------------------------+
| `Normalized Advantage Function`_ | NAF |
+--------------------------------------------------------+-------------------------+
| `Stochastic Value Gradients`_ | SVG(inf)/SVG(1)/SoftSVG |
+--------------------------------------------------------+-------------------------+
| `Soft Actor-Critic`_ | SoftAC |
+--------------------------------------------------------+-------------------------+
| `Streamlined Off-Policy`_ (DDPG) | SOP |
+--------------------------------------------------------+-------------------------+
| `Model-Based Policy Optimization`_ | MBPO |
+--------------------------------------------------------+-------------------------+
| `Model-based Action-Gradient-Estimator`_ | MAGE |
+--------------------------------------------------------+-------------------------+

.. _`Actor Critic using Kronecker-factored Trust Region`: https://arxiv.org/abs/1708.05144
.. _`Trust Region Policy Optimization`: http://proceedings.mlr.press/v37/schulman15.html
.. _`Normalized Advantage Function`: http://proceedings.mlr.press/v48/gu16.html
.. _`Stochastic Value Gradients`: http://papers.nips.cc/paper/5796-learning-continuous-control-policies-by-stochastic-value-gradients
.. _`Soft Actor-Critic`: http://proceedings.mlr.press/v80/haarnoja18b.html
.. _`Model-Based Policy Optimization`: http://arxiv.org/abs/1906.08253
.. _`Streamlined Off-Policy`: https://arxiv.org/abs/1910.02208
.. _`Model-based Action-Gradient-Estimator`: https://arxiv.org/abs/2004.14309

Command-line interface
----------------------

.. role:: bash(code)
:language: bash

For a high-level description of the available utilities, run :bash:`raylab --help`

.. code:: bash

Usage: raylab [OPTIONS] COMMAND [ARGS]...

RayLab: Reinforcement learning algorithms in RLlib.

Options:
--help Show this message and exit.

Commands:
dashboard Launch the experiment dashboard to monitor training progress.
episodes Launch the episode dashboard to monitor state and action...
experiment Launch a Tune experiment from a config file.
find-best Find the best experiment checkpoint as measured by a metric.
info View information about an agent's config parameters.
rollout Wrap `rllib rollout` with customized options.
test-module Launch dashboard to test generative models from a checkpoint.

Packages
--------

The project is structured as follows
::