DeepMind Lab2D
A learning environment written in C++ and Lua for the creation of grid worlds.
About
DeepMind Lab2D is a system for the creation of 2D environments for machine
learning. The main goals of the system are ease of use and performance: The
environments are "grid worlds", which are defined with a combination of simple
text-based maps for the layout of the world, and Lua code for its behaviour.
Machine learning agents interact with these environments through one of two
APIs, the Python dm_env API or a custom
C API (which is also used by DeepMind Lab).
Multiple agents are supported.
If you use DeepMind Lab2D in your research and would like to cite it, we suggest you cite the accompanying whitepaper.
Installation
DeepMind Lab2d is available on PyPI and can be installed using:
pip install dmlab2d
dmlab2d is distributed as pre-built wheels for Linux and macOS. If there is no
appropriate wheel for your platform, you will need to build it from source. See
install.sh for an example installation script that can be
adapted to your setup.
Getting started
We provide an example "random" agent in python/random_agent, which performs
random actions. This can be used as a base for creating your own agents, and as
a simple tool to preview an environment.
bazel run -c opt dmlab2d/random_agent -- --level_name=clean_up
External dependencies, prerequisites and porting notes
DeepMind Lab2D depends on a few external software libraries, which we ship in several different ways:
-
The
dm_env,eigen,luajit,lua5.1,lua5.2,luajit,pngandzliblibraries are referenced as external Bazel sources, and Bazel BUILD files are provided where needed. The dependent code itself should be fairly portable, and the BUILD rules we ship are specific to Linux and MacOS, both on x86-64 and aarch64). To build on a different platform you will most likely have to edit those BUILD files. -
A "generic reinforcement learning API" is included in
//third_party/rl_api. -
Several additional libraries are required but are not shipped in any form; they must be present on your system:
Python 3.8or above withNumPy,PyGame, andpackaging.
As time goes by, the continuous integration tests will only test on current versions of Python, and we will only build PyPi/PIP wheels for a few recent versions, but the code should be compatible at least back to Python 3.8.
The build rules are using a few compiler settings that are specific to GCC/Clang. If some flags are not recognized by your compiler (typically those would be specific warning suppressions), you may have to edit those flags.
Disclaimer
This is not an official Google product.
Release files for dmlab2d 1.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
Total release size: 31.7 MB
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