gym-anm是用于设计强化学习（RL）环境的框架，该环境为配电网络中的“主动网络管理（ANM）”任务建模。 它建立在OpenAI Gym工具包之上。
设计“ gym-anm”框架是弥合RL研究与电力系统管理之间的鸿沟，RL研究人员试图提供一个易于使用的环境库，以对电网中的决策任务进行建模。
主要特点包括：
- Very little background in electricity systems modelling it required. This makes gym-anm an ideal starting point for RL students and researchers looking to enter the field.
- The environments (tasks) generated by gym-anm follow the OpenAI Gym framework, with which a large part of the RL community is already familiar.
- The flexibility of gym-anm, with its different customizable components, makes it a suitable framework to model a wide range of ANM tasks, from simple ones that can be used for educational purposes, to complex ones designed to conduct advanced research.
安装使用
Requirements
gym-anm requires Python 3.7+ and can run on Linux, MaxOS, and Windows. We recommend installing gym-anm in a Python environment (e.g., virtualenv or conda).
# pip安装
pip install gym-anm
Building from source
# 源代码安装
git clone https://github.com/robinhenry/gym-anm.git
cd gym-anm
pip install -e .
举个例子
import gym
import time
env = gym.make('gym_anm:ANM6Easy-v0')
o = env.reset()
for i in range(100):
a = env.action_space.sample()
o, r, done, info = env.step(a)
env.render()
time.sleep(0.5) # otherwise the rendering is too fast for the human eye.
引用
To cite this project in publications, cite the original paper:
@misc{henry2021gymanm,
title={Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systems},
author={Robin Henry and Damien Ernst},
year={2021},
eprint={2103.07932},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
原文地址
