id: 06066558
dt: a
an: 06066558
au: Martinez-Gil, Francisco; Lozano, Miguel; Fernández, Fernando
ti: Multi-agent reinforcement learning for simulating pedestrian navigation.
so: Vrancx, Peter (ed.) et al., Adaptive and learning agents. International
    workshop, ALA 2011, held at AAMAS 2011, Taipei, Taiwan, May 2, 2011.
    Revised selected papers. Berlin: Springer (ISBN 978-3-642-28498-4/pbk).
    Lecture Notes in Computer Science 7113. Lecture Notes in Artificial
    Intelligence, 54-69 (2012).
py: 2012
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-28499-1_4
ab: Summary: In this paper we introduce a Multi-agent system that uses
    Reinforcement Learning (RL) techniques to learn local navigational
    behaviors to simulate virtual pedestrian groups. The aim of the paper
    is to study empirically the validity of RL to learn agent-based
    navigation controllers and their transfer capabilities when they are
    used in simulation environments with a higher number of agents than in
    the learned scenario. Two RL algorithms which use Vector Quantization
    (VQ) as the generalization method for the space state are presented.
    Both strategies are focused on obtaining a good vector quantizier that
    generalizes adequately the state space of the agents. We empirically
    state the convergence of both methods in our navigational Multi-agent
    learning domain. Besides, we use validation tools of pedestrian models
    to analyze the simulation results in the context of pedestrian
    dynamics. The simulations carried out, scaling up the number of agents
    in our environment (a closed room with a door through which the agents
    have to leave), have revealed that the basic characteristics of
    pedestrian movements have been learned.
rv: