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You're reading from  Deep Reinforcement Learning Hands-On. - Second Edition

Product typeBook
Published inJan 2020
Reading LevelIntermediate
PublisherPackt
ISBN-139781838826994
Edition2nd Edition
Languages
Python
Tools
KerasTensorFlow
Concepts
Deep Reinforcement Learning
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Author (1)
Maxim Lapan
Maxim Lapan
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Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan

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The MAgent environment

Before we jump into our first MARL example, I will describe our environment to experiment with.

Installation

If you want to play with MARL, your choice is a bit limited. All the environments that come with Gym support only one agent. There are some patches for Atari Pong, to switch it into two-player mode, but they are not standard and are an exception rather than the rule.

DeepMind, together with Blizzard, has made StarCraft II publicly available (https://github.com/deepmind/pysc2) and it makes for a very interesting and challenging environment for experimentation. However, for somebody who is taking their first steps in MARL, it might be too complex. In that regard, I found the MAgent environment from Geek.AI (https://github.com/geek-ai/MAgent) perfectly suitable: it is simple, fast, and has minimal dependency, but it still allows you to simulate different multi-agent scenarios for experimentation. It doesn't provide a Gym-compatible API, but...

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Deep Reinforcement Learning Hands-On. - Second Edition
Published in: Jan 2020Publisher: PacktISBN-13: 9781838826994
© 2020 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan

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