.. raw:: html

    <bold><span style="color: red">This repo is still pre-release. The documentation is still incomplete. </span></bold>

########
megastep
########

**megastep** helps you build 1-million FPS reinforcement learning environments *on a single GPU*.

Examples
********
**Explorer**: 180k FPS, :github:`100 lines of Python <megastep/demo/envs/explorer.py>`.

.. raw:: html

    <video controls src="_static/explorer.mp4" autoplay loop muted type="video/mp4" width=640></video>

**Deathmatch**: 1.2m FPS, :github:`150 lines of Python <megastep/demo/envs/deathmatch.py>`.

.. raw:: html

    <video controls src="_static/deathmatch.mp4" autoplay loop muted type="video/mp4" width=640></video>

Quoted FPS are for a single RTX 2080 Ti and random actions; visualizations are with a 256-neuron LSTM.

Features
********
 * Run thousands of environments in parallel, entirely on the GPU.
 * Write your own environments using PyTorch alone, no CUDA necessary.
 * 1D observations. The world is more interesting horizontally than vertically.
 * One or many agents, and one or many cameras per agent.
 * A database of 5000 home layouts to explore, based on `Cubicasa5k <https://github.com/CubiCasa/CubiCasa5k>`_.
 * A minimal, modular library. Not a framework.
 * (In progress) Extensive documentation, tutorials and explanations. 

.. _install:

Install
*******
If you're working on an Ubuntu machine with CUDA already set up and Python 3.6 or higher, it should be as simple as

.. code-block:: shell

    pip install megastep[cubicasa,rebar]

To check everything works, run ::

    from megastep.demo import *
    env = explorer.Explorer(1)
    agent = Agent(env).cuda()
    demo(env=env, agent=agent, length=64)

**The first time you import megastep on your machine will be slow** as it compiles the C++ side of things.

Getting Started
***************
Having :ref:`installed megastep <install>`, you have a few choices. 

If you'd like something comprehensive, read :ref:`Writing a Minimal Environment <minimal-env>`.

If you'd like a more self-propelled introduction, read :ref:`Playing With Megastep <playing>`.

Or, you can simply browse the :github:`demos <megastep/demo/__init__.py>`.

.. _support:

Support
*******
I am an independent researcher, so just how much support I can give is limited. But you're absolutely welcome to `open
a GitHub issue <https://github.com/andyljones/megastep/issues>`_ or `see if I'm around on
the RL Discord <https://www.reddit.com/r/reinforcementlearning/comments/jawm37/official_reinforcement_learning_discord/>`_. At the very least I'll try to point you in the right direction.

You can also find me `on Twitter <https://twitter.com/andy_l_jones>`_ or you can `email me <andyjones.ed@gmail.com>`_. 

Of the options, a GitHub issue is likely the best choice as if you have a question, there are likely other people out
there with the same exact question! And if they don't, maybe someone else will have an answer for you.

Index
*****
.. toctree::
    :maxdepth: 2
    
    tutorials/index
    concepts
    faq

.. toctree::
    :maxdepth: 3

    reference