Comparing Reinforcement Learning Algorithms for a Trip Building Task: a Multi-objective Approach Using Non-Local Information.

Saved in:

Bibliographic Details
Title:	Comparing Reinforcement Learning Algorithms for a Trip Building Task: a Multi-objective Approach Using Non-Local Information.
Authors:	Gobbi, Henrique U.¹ hugobbi@inf.ufrgs.br, Dytz dos Santos, Guilherme¹ gdsantos@inf.ufrgs.br, Bazzan, Ana L. C.¹ bazzan@inf.ufrgs.br
Source:	Computer Science & Information Systems. Jan2024, Vol. 21 Issue 1, p291-308. 18p.
Subjects:	Stochastic learning models, Machine learning, Reinforcement learning, Travel time (Traffic engineering), Carbon monoxide
Abstract:	Using reinforcement learning (RL) to support agents in making decisions that consider more than one objective poses challenges. We formulate the problem of multiple agents learning how to travel from A to B as a reinforcement learning task modeled as a stochastic game, in which we take into account: (i) more than one objective, (ii) non-stationarity, (iii) communication of local and non-local information among the various actors. We use and compare RL algorithms, both for the single objective (Q-learning), as well as for multiple objectives (Pareto Qlearning), with and without non-local communication. We evaluate these methods in a scenario in which hundreds of agents have to learn how to travel from their origins to their destinations, aiming at minimizing their travel times, as well as the carbon monoxide vehicles emit. Results show that the use of non-local communication reduces both travel time and emissions. [ABSTRACT FROM AUTHOR]
	Copyright of Computer Science & Information Systems is the property of ComSIS Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Engineering Source

Description
Abstract:	Using reinforcement learning (RL) to support agents in making decisions that consider more than one objective poses challenges. We formulate the problem of multiple agents learning how to travel from A to B as a reinforcement learning task modeled as a stochastic game, in which we take into account: (i) more than one objective, (ii) non-stationarity, (iii) communication of local and non-local information among the various actors. We use and compare RL algorithms, both for the single objective (Q-learning), as well as for multiple objectives (Pareto Qlearning), with and without non-local communication. We evaluate these methods in a scenario in which hundreds of agents have to learn how to travel from their origins to their destinations, aiming at minimizing their travel times, as well as the carbon monoxide vehicles emit. Results show that the use of non-local communication reduces both travel time and emissions. [ABSTRACT FROM AUTHOR]
ISSN:	18200214
DOI:	10.2298/CSIS221210072G