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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 wayfindingdblp:conf/woa/AlbericciCGV21 fatcat:6jtxyoin7nhgvh56kvsowamwha