Recent publications

A Reinforcement Learning Approach for Multiagent Navigation Francisco Martinez-Gil, Fernando Barber, Miguel Lozano, Francisco Grimaldo, Fernando Fernández. ICAART 2010 - Proceedings of the International Conference on Agents and Artificial Intelligence, Volume 1 - Artificial Intelligence, Valencia, Spain, January 22-24, 2010. INSTICC Press 2010, ISBN 978-989-674-021-4

Multi-Agent Reinforcement Learning for Simulating Pedestrian Navigation Francisco Martinez-Gil, Miguel Lozano, Fernando Fernández. Adaptive and Learning Agents Workshop at AAMAS (ALA'2011). LNAI 7113 (P. Vrancx, M. Knudson, M. Grzes Eds.) . Pags.54-69. Springer. 2012

Calibrating a motion model based on reinforcement learning for pedestrian simulation Francisco Martinez-Gil, Miguel Lozano, Fernando Fernández. ACM SIGGRAPH Conference on Motion in Games (MIG 2012) Rennes (France). LNCS 7660 (M. Kallmann, K. Bekris eds.) Pages 302-313. Springer. 2012.

Emergent collective behaviors in a multi-agent reinforcement learning based pedestrian simulation Francisco Martinez-Gil, Miguel Lozano, Fernando Fernández. Extended abstracts booklet of the First Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM2013). Princeton University. New Jersey. 25-27 October 2013.
Accepted as a full paper in the AAMAS 2014 Workshop: Multi-Agent-Based Simulation (MABS 2014) Paris, France. 2014

Strategies for simulating pedestrian navigation with multiple reinforcement learning agents Francisco Martinez-Gil, Miguel Lozano, Fernando Fernández. Autonomous Agents and Multi-Agent Systems. 2014. Springer. DOI: 10.1007/s10458-014-9252-6 (In Press)

MARL-Ped: a Multi-Agent Reinforcement Learning Based Framework to Simulate Pedestrian Groups Francisco Martinez-Gil, Miguel Lozano, Fernando Fernández. Simulation Modelling Practice and Theory. 47: 259-275 (2014). Elsevier.

Strategies for simulating pedestrian navigation with multiple reinforcement learning agents Francisco Martinez-Gil, Miguel Lozano, Fernando Fernández. Autonomous Agents and Multi-Agent Systems. 29 (1): 98-130 (2015). Springer. DOI: 10.1007/s10458-014-9252-6 

Emergent behaviors and scalability for multi-agent reinforcement learning-based pedestrian models  Francisco Martinez-Gil, Miguel Lozano, Fernando Fernández. Simulation Modelling Practice and Theory 74: 117-133 (2017). Elsevier.

Modeling, Evaluation and Scale on Artificial Pedestrians: A literature review  Francisco Martinez-Gil, Miguel Lozano, Ignacio García, Fernando Fernández. ACM Computing Surveys (CSUR) 50 (5): Article 72 (2017). ACM.

Using Inverse Reinforcement Learning with Real Trajectories to Get More Trustworthy Pedestrian Simulations. F Martinez-Gil, M Lozano, I García-Fernández, P Romero, D Serra. Mathematics 8 (9), (2020) 

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