RT Journal Article
T1 Emergent behaviors and scalability for multi-agent reinforcement learning-based pedestrian models
A1 Martínez Gil, Francisco
A1 Lozano, Miguel
A1 Fernández Rebollo, Fernando
AB This paper analyzes the emergent behaviors of pedestrian groups that learn through the multiagent reinforcement learning model developed in our group. Five scenarios studied in the pedestrian model literature, and with different levels of complexity, were simulated in order to analyze the robustness and the scalability of the model. Firstly, a reduced group of agents must learn by interaction with the environment in each scenario. In this phase, each agent learns its own kinematic controller, that will drive it at a simulation time. Secondly, the number of simulated agents is increased, in each scenario where agents have previously learnt, to test the appearance of emergent macroscopic behaviors without additional learning. This strategy allows us to evaluate the robustness and the consistency and quality of the learned behaviors. For this purpose several tools from pedestrian dynamics, such as fundamental diagrams and density maps, are used. The results reveal that the developed model is capable of simulating human-like micro and macro pedestrian behaviors for the simulation scenarios studied, including those where the number of pedestrians has been scaled by one order of magnitude with respect to the situation learned.
PB Elsevier
SN 1569-190X
YR 2017
FD 2017-05-01
LK https://hdl.handle.net/10016/29470
UL https://hdl.handle.net/10016/29470
LA eng
NO This work has been supported by grant TIN2015-65686-C5-1-R of Ministerio de Economía y Competitividad.
DS e-Archivo
RD 17 jul. 2024