项目介绍

tomalner18/MAax 是一个托管在 GitHub 上的开源项目,主要使用 Python 编写,目前获得 1 个 Star、0 次 Fork。

仓库全名
tomalner18/MAax
Star 数
1

README 摘要

MAax Procedural Environment Generation for Accelerated Multi-Agent Reinforcement Learning Installation This repository depends on the mujoco-worldgen package. We provide a fork of this repository and thank the authors for their contribution. Install the necessary dependencies: Use Environment creation begins by defining an instance of the Base class. From there you can then add desired modules to introduce objects…

完整内容见 GitHub 原始 README 或本页 。

常见问题

MAax 用什么语言开发?

主要使用 Python 编写,目前有 1 个 Star。

如何获取 MAax 的源码?

可以直接克隆仓库:git clone https://github.com/tomalner18/MAax.git,默认分支为 master;安装与运行步骤见仓库 README。

MAax 最近还在维护吗?

仓库最近一次更新为 2024年5月29日,当前没有未关闭的 Issue;Star 数为 1,可作为社区活跃度的参考。

关于

GitHub 原始描述(英文)

Procedural Environment Generation for Accelerated Multi-Agent Reinforcement Learning

描述取自 GitHub 官方 API,保持仓库作者书写的原文。

语言占比

Python

贡献者

README

MAax

Procedural Environment Generation for Accelerated Multi-Agent Reinforcement Learning

Installation

This repository depends on the mujoco-worldgen package. We provide a fork of this repository and thank the authors for their contribution. Install the necessary dependencies:

pip install -r worldgen/requirements.txt
pip install -e worldgen/
pip install -e maax/

Use

Environment creation begins by defining an instance of the Base class. From there you can then add desired modules to introduce objects to the environment (Walls, Boxes, Ramps).

For adding environmental mechanics, we recommend implementing a new Wrapper inheriting from our custom Wrapper class designed for Multi-agent environments in Brax.

An example of environment creation can be found in the example.ipynb notebook. This notebook is currently parameterised to create a standard Hide-and_seek environment with 2 hiders, 2 seekers, 2 boxes, 1 ramp and a small room sectioned by walls.

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