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Evaluating the learning and performance characteristics of self-organizing systems with different task features

Published online by Cambridge University Press:  27 December 2021

Hao Ji  and
Yan Jin Open the ORCID record for Yan Jin [Opens in a new window]
Hao Ji
Affiliation:
Department of Aerospace and Mechanical Engineering, University of Southern California, 3650 McClintock Avenue, OHE 400, Los Angeles, CA90089-1453, USA
Yan Jin*
Affiliation:
Department of Aerospace and Mechanical Engineering, University of Southern California, 3650 McClintock Avenue, OHE 400, Los Angeles, CA90089-1453, USA
*
Author for correspondence: Yan Jin, E-mail: [email protected]
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Abstract

Self-organizing systems (SOS) are developed to perform complex tasks in unforeseen situations with adaptability. Predefining rules for self-organizing agents can be challenging, especially in tasks with high complexity and changing environments. Our previous work has introduced a multiagent reinforcement learning (RL) model as a design approach to solving the rule generation problem of SOS. A deep multiagent RL algorithm was devised to train agents to acquire the task and self-organizing knowledge. However, the simulation was based on one specific task environment. Sensitivity of SOS to reward functions and systematic evaluation of SOS designed with multiagent RL remain an issue. In this paper, we introduced a rotation reward function to regulate agent behaviors during training and tested different weights of such reward on SOS performance in two case studies: box-pushing and T-shape assembly. Additionally, we proposed three metrics to evaluate the SOS: learning stability, quality of learned knowledge, and scalability. Results show that depending on the type of tasks; designers may choose appropriate weights of rotation reward to obtain the full potential of agents’ learning capability. Good learning stability and quality of knowledge can be achieved with an optimal range of team sizes. Scaling up to larger team sizes has better performance than scaling downwards.

Keywords

Complex systemdeep q-learningrobustnessscalabilityself-organizing system
Type
Research Article
Information
AI EDAM , Volume 35 , Issue 4 , November 2021 , pp. 404 - 422
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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