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Status: Archive (code is provided as-is, no updates expected)
RoboSumo
This repository contains a set of competitive multi-agent environments used in the paper Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments.
<table> <tr> <td><img src="/assets/robosumo1.gif?raw=true" width="400"></td> <td><img src="/assets/robosumo2.gif?raw=true" width="400"></td> </tr> </table>Installation
RoboSumo depends on numpy, gym, and 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 tensorflow>=1.1.0 and click.
The requirements can be installed via pip as follows:
$ pip install -r requirements.txt
To install RoboSumo, clone the repository and run pip install:
$ git clone https://github.com/openai/robosumo
$ cd robosumo
$ pip install -e .
Demos
You can run demos of the environments using demos/play.py script:
$ 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.