Computer Science > Multiagent Systems
[Submitted on 20 Jul 2021 (v1), last revised 17 Jan 2022 (this version, v5)]
Title:Improved Reinforcement Learning in Cooperative Multi-agent Environments Using Knowledge Transfer
View PDFAbstract:Nowadays, cooperative multi-agent systems are used to learn how to achieve goals in large-scale dynamic environments. However, learning in these environments is challenging: from the effect of search space size on learning time to inefficient cooperation among agents. Moreover, reinforcement learning algorithms may suffer from a long time of convergence in such environments. In this paper, a communication framework is introduced. In the proposed communication framework, agents learn to cooperate effectively and also by introduction of a new state calculation method the size of state space will decline considerably. Furthermore, a knowledge-transferring algorithm is presented to share the gained experiences among the different agents, and develop an effective knowledge-fusing mechanism to fuse the knowledge learnt utilizing the agents' own experiences with the knowledge received from other team members. Finally, the simulation results are provided to indicate the efficacy of the proposed method in the complex learning task. We have evaluated our approach on the shepherding problem and the results show that the learning process accelerates by making use of the knowledge transferring mechanism and the size of state space has declined by generating similar states based on state abstraction concept.
Submission history
From: Amin Nikanjam [view email][v1] Tue, 20 Jul 2021 23:42:39 UTC (478 KB)
[v2] Sun, 25 Jul 2021 14:17:50 UTC (477 KB)
[v3] Sun, 3 Oct 2021 14:04:33 UTC (1,749 KB)
[v4] Sun, 28 Nov 2021 22:55:52 UTC (1,345 KB)
[v5] Mon, 17 Jan 2022 19:23:02 UTC (2,257 KB)
Current browse context:
cs.MA
References & Citations
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)
Connected Papers (What is Connected Papers?)
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.