Communication and machine learning in urban mobility: a multiagent and multiobjective approach

• Grant number: 20/05165-1
• Support Opportunities: Regular Research Grants
• Start date: February 01, 2021
• End date: July 31, 2023
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Agreement: MCTI/MC
• Principal Investigator: Ana Lúcia Cetertich Bazzan
• Grantee: Ana Lúcia Cetertich Bazzan
• Host Institution: Instituto de Informática. Universidade Federal do Rio Grande do Sul (UFRGS). Ministério da Educação (Brasil). Porto Alegre , SP, Brazil
• Associated researchers: Anderson Rocha Tavares ; Ann Nowé ; Gabriel de Oliveira Ramos
• Associated scholarship(s): 22/02103-0 - Communication and machine learning in urban mobility: a multiagent and multiobjective approach, BP.TT; 22/03607-2 - Communication and machine learning in urban mobility: a multiagent and multiobjective approach, BP.TT; 21/12672-0 - Communication and machine learning in urban mobility: a multi-agent and multi-objective approach, BP.TT; 21/05093-3 - Communication and machine learning in urban mobility: a multiagent and multiobjective approach, BP.TT

Abstract

Machine learning is being used in the area of urban mobility. In particular, reinforcement learning (RL) is a technique that is frequently used since it allows agents to adapt to the state of the traffic. However, the literature on RL rarely discusses works that tackle multiobjective decision making, that are key in this domain. This way, the goal of this project is to develop a framework, as well as methods to take into account multiobjective decision making when agents of various types (drivers, traffic signals) are deciding what actions to take. In order to improve the efficiency of the learning process, we propose the use of transfer learning techniques. To this aim, new methods will be developed, using car 2 car and other forms of communication. (AU)

Scientific publications

(9)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

VAMPLEW, PETER; SMITH, BENJAMIN J.; KALLSTROM, JOHAN; RAMOS, GABRIEL; RADULESCU, ROXANA; ROIJERS, DIEDERIK M.; HAYES, CONOR F.; HEINTZ, FREDRIK; MANNION, PATRICK; LIBIN, PIETER J. K.; et al. Scalar reward is not enough: a response to Silver, Singh, Precup and Sutton (2021). AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS, v. 36, n. 2, p. 19-pg., 2022-10-01. (20/05165-1)

RODRIGUES NETO, JOAO B.; RAMOS, GABRIEL DE O.; IEEE. An Interpolated Approach for Active Debris Removal. 2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), v. N/A, p. 6-pg., 2022-01-01. (20/05165-1)

ALEGRE, LUCAS N.; BAZZAN, ANA L. C.; DA SILVA, BRUNO C.; CHAUDHURI, K; JEGELKA, S; SONG, L; SZEPESVARI, C; NIU, G; SABATO, S. Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer. INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162, v. N/A, p. 20-pg., 2022-01-01. (20/05165-1)

DA SILVA, JUAREZ MACHADO; RAMOS, GABRIEL DE OLIVEIRA; BARBOSA, JORGE LUIS VICTORIA. Multi-Objective Decision-Making Meets Dynamic Shortest Path: Challenges and Prospects. ALGORITHMS, v. 16, n. 3, p. 19-pg., 2023-03-01. (20/05165-1)

COLOMBELLI, FELIPE; MATTER, VITOR KEHL; GRISCI, BRUNO IOCHINS; LIMA, LEOMAR; HEINEN, KARINE; BORGES, MARCIO; RIGO, SANDRO JOSE; VICTORIA BARBOSA, JORGE LUIS; RIGHI, RODRIGO DA ROSA; DA COSTA, CRISTIANO ANDRE; et al. Multi-objective prioritization for data center vulnerability remediation. 2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), v. N/A, p. 8-pg., 2022-01-01. (20/05165-1)

BOECHEL, TIAGO; POLICARPO, LUCAS MICOL; RAMOS, GABRIEL DE OLIVEIRA; RIGHI, RODRIGO DA ROSA; SINGH, DHANANJAY. Prediction of Harvest Time of Apple Trees: An RNN-Based Approach. ALGORITHMS, v. 15, n. 3, p. 18-pg., 2022-03-01. (20/05165-1)

HAYES, CONOR F.; RADULESCU, ROXANA; BARGIACCHI, EUGENIO; KALLSTROM, JOHAN; MACFARLANE, MATTHEW; REYMOND, MATHIEU; VERSTRAETEN, TIMOTHY; ZINTGRAF, LUISA M.; DAZELEY, RICHARD; HEINTZ, FREDRIK; et al. A practical guide to multi-objective reinforcement learning and planning. AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS, v. 36, n. 1, p. 59-pg., 2022-04-01. (20/05165-1)

DA SILVA, JUAREZ M.; RAMOS, GABRIEL DE O.; BARBOSA, JORGE L., V; IEEE. The multi-objective dynamic shortest path problem. 2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), v. N/A, p. 8-pg., 2022-01-01. (20/05165-1)

SCHREIBER, LINCOLN V.; ALEGRE, LUCAS N.; BAZZAN, ANA. L. C.; RAMOS, GABRIEL DE O.; IEEE. On the Explainability and Expressiveness of Function Approximation Methods in RL-Based Traffic Signal Control. 2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., 2022-01-01. (20/05165-1)