Status: Archive (code is provided as-is, no updates expected)
Environment generation code for Emergent Tool Use From Multi-Agent Autocurricula (blog)
This repository depends on the mujoco-worldgen package. You will need to clone the mujoco-worldgen repository and install it and its dependencies:
pip install -r mujoco-worldgen/requirements.txt
pip install -e mujoco-worldgen/
pip install -e multi-agent-emergence-environments/
This repository has been tested only on Mac OS X and Ubuntu 16.04 with Python 3.6
Environment construction works in the following way: You start from the Base environment (defined in mae_envs/envs/base.py) and then you add environment modules (e.g. Boxes, Ramps, RandomWalls, etc.) and then wrappers on top. You can see examples in the mae_envs/envs folder.
If you want to construct a new environment, we highly recommend using the above paradigm in order to minimize code duplication. If you need new objects or game dynamics that don't already exist in this codebase, add them in via a new EnvModule class or a gym.Wrapper class rather than subclassing Base (or mujoco-worldgen's Env class). In general, EnvModules should be used for adding objects or sites to the environment, or otherwise modifying the mujoco simulator; wrappers should be used for everything else (e.g. adding rewards, additional observations, or implementing game mechanics like Lock and Grab).
The environments defined in this repository are:
Hide and seek - mae_envs/envs/hide_and_seek.py - The Hide and Seek environment described in the paper. This encompasses the random rooms, quadrant and food versions of the game (you can switch between them by changing the arguments given to the make_env function in the file)
Box locking - mae_envs/envs/box_locking.py - Encompasses the Lock and Return and Sequential Lock transfer tasks described in the paper.
Blueprint Construction - mae_envs/envs/blueprint_construction.py
Shelter Construction - mae_envs/envs/shelter_construction.py
You can test out environments by using the bin/examine script. Example usage: bin/examine.py base.
You can also use bin/examine to play a saved policy on an environment. There are several environment jsonnets and policies in the examples folder. Example usage:
bin/examine.py examples/hide_and_seek_quadrant.jsonnet examples/hide_and_seek_quadrant.npz
Note that to be able to play saved policies, you will need to install a few additional packages. You can do this via
pip install -r multi-agent-emergence-environments/requirements_ma_policy.txt