Status: Archive (code is provided as-is, no updates expected)
This repository contains a set of competitive multi-agent environments used in the paper Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments.
RoboSumo depends on
mujoco_py>=1.5 (if you haven't used MuJoCo before, please refer to the installation guide).
Running demos with pre-trained policies additionally requires
The requirements can be installed via pip as follows:
$ pip install -r requirements.txt
To install RoboSumo, clone the repository and run
$ git clone https://github.com/openai/robosumo $ cd robosumo $ pip install -e .
You can run demos of the environments using
$ python demos/play.py
The script allows you to select different opponents as well as different policy architectures and versions for the agents. For details, please refer to the help:
$ python demos/play.py --help Usage: play.py [OPTIONS] Options: --env TEXT Name of the environment. [default: RoboSumo-Ant-vs-Ant-v0] --policy-names [mlp|lstm]... Policy names. [default: mlp, mlp] --param-versions INTEGER... Policy parameter versions. [default: 1, 1] --max_episodes INTEGER Number of episodes. [default: 20] --help Show this message and exit.