Artificial Intelligence


Multi-Agent Reinforcement Learning - From Game Theory to Organic Computing

Authors: Maurice Gerczuk

Complex systems consisting of multiple agents that interact both with each other as well as their environment can often be found in both nature and technical applications. This paper gives an overview of important Multi-Agent Reinforcement Learning (MARL) concepts, challenges and current research directions. It shortly introduces traditional reinforcement learning and then shows how MARL problems can be modelled as stochastic games. Here, the type of problem and the system configuration can lead to different algorithms and training goals. Key challenges such as the curse of dimensionality, choosing the right learning goal and the coordination problem are outlined. Especially, aspects of MARL that have previously been considered from a critical point of view are discussed with regards to if and how the current research has addressed these criticism or shifted their focus. The wide range of possible MARL applications is hinted at by examples from recent research. Further, MARL is assessed from an Organic Computing point of view where it takes a central role in the context of self-learning and self-adapting systems.

Comments: 6 Pages.

Download: PDF

Submission history

[v1] 2019-03-01 03:32:14

Unique-IP document downloads: 34 times is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. will not be responsible for any consequences of actions that result from any form of use of any documents on this website.

Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.

comments powered by Disqus