@inproceedings{0e25a29418824572ba682f3ed06a6799,
title = "Flow-shop robotic scheduling with collaborative reinforcement learning",
abstract = "A collaborative reinforcement learning (RL) method for minimizing make-span in a robotic flow-shop scheduling problem is presented. The robot can operate either autonomously (no adviser) or semi-autonomously (with adviser). In autonomous mode, the robot uses RL ε-greedy selection scheme. In semi-autonomous mode a collaborative agent (adviser) provides advice to the robot. The robot is endowed with three cognitive abilities: (i) ability to assess its own performance, using an adaptive performance threshold to switch between collaborative modes, (ii) short term ability to assess good and bad advice, and to accept or reject it, (iii) and long term ability to assess advisor's skill levels, and discontinue collaborating with novice advisors. Adviser's behaviors are simulated by various skill levels, represented by softmax action selection distributions. The collaborative robot-adviser system average error was, at the most, 9.4% within a lower-bound value. An expert adviser was found to accelerate the robot learning process.",
keywords = "Collaboration, Flow-Shop, Job transfer robots, Reinforcement learning, Scheduling",
author = "Helman Stern and Kfir Arviv and Yael Edan",
year = "2011",
month = jan,
day = "1",
language = "American English",
series = "21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 - Conference Proceedings",
editor = "Tobias Krause and Dieter Spath and Rolf Ilg",
booktitle = "21st International Conference on Production Research",
note = "21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 ; Conference date: 31-07-2011 Through 04-08-2011",
}
