Reinforcement Learning represents a way to train an agent situated in an environment what to do to maximise an accumulated numerical reward signal (received by the environment as a feedback to every chosen action). Within this paper we explore the possibility to apply this approach to pedestrian modelling: pedestrians generally do not exhibit an optimal behaviour, therefore we carefully defined a reward function (combining contributions related to proxemics, goal orientation, basic wayfinding considerations), but also a particular training curriculum, a set of scenarios of growing difficulty supporting the incremental acquisition of proper orientation, walking, and pedestrian interaction competences. The paper will describe the fundamental elements of the approach, its implementation within a software framework employing Unity and ML-Agents, describing the promising achieved simulation results.

Albericci, T., Cecconello, T., Gibertini, A., Vizzari, G. (2021). A curriculum-based reinforcement learninig approach to pedestrian simulation. In Proceedings of the 22nd Workshop "From Objects to Agents", Bologna, Italy, September 1-3, 2021 (pp.224-240). CEUR-WS.

A curriculum-based reinforcement learninig approach to pedestrian simulation

Cecconello, T;Vizzari, G
2021

Abstract

Reinforcement Learning represents a way to train an agent situated in an environment what to do to maximise an accumulated numerical reward signal (received by the environment as a feedback to every chosen action). Within this paper we explore the possibility to apply this approach to pedestrian modelling: pedestrians generally do not exhibit an optimal behaviour, therefore we carefully defined a reward function (combining contributions related to proxemics, goal orientation, basic wayfinding considerations), but also a particular training curriculum, a set of scenarios of growing difficulty supporting the incremental acquisition of proper orientation, walking, and pedestrian interaction competences. The paper will describe the fundamental elements of the approach, its implementation within a software framework employing Unity and ML-Agents, describing the promising achieved simulation results.
paper
Agent-based simulation; Curriculum learning; Pedestrian simulation; Reinforcement learning;
English
22nd Workshop "From Objects to Agents", WOA 2021 - 1 September 2021 through 3 September 2021
2021
Calegari, R; Ciatto, G; Denti, E; Omicini, A; Sartor, G
Proceedings of the 22nd Workshop "From Objects to Agents", Bologna, Italy, September 1-3, 2021
2021
2963
224
240
none
Albericci, T., Cecconello, T., Gibertini, A., Vizzari, G. (2021). A curriculum-based reinforcement learninig approach to pedestrian simulation. In Proceedings of the 22nd Workshop "From Objects to Agents", Bologna, Italy, September 1-3, 2021 (pp.224-240). CEUR-WS.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/331681
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