Borja Fernandez‐Gauna

About

Borja Fernandez‐Gauna is a leading researcher in multi-agent and robot reinforcement learning, with a focus on the challenging domain of linked multicomponent robotic systems (L-MCRS)—particularly hose transport and deployment. His work addresses two fundamental hurdles in multi-agent reinforcement learning (MARL): the curse of dimensionality and environmental non-stationarity. He introduced the Distributed Round-Robin Q-Learning algorithm, formally proving its convergence for multirobot hose control (22 citations). Fernandez‐Gauna has also pioneered the use of Extreme Learning Machines (ELMs) to model state-action value functions, drastically reducing data requirements for reinforcement learning in single-robot hose transport (17 citations). His research on safe veto policies and undesired state-action prediction enhances the reliability of autonomous control in complex, nonlinear robotic systems. With over 130 citations across his top papers, Fernandez‐Gauna’s contributions are foundational for advancing autonomous multirobot coordination, offering scalable and robust solutions that bridge theory and practical deployment in real-world robotic tasks.

Research Focus

Key Achievements

Top Papers

1. 1
   Learning Multirobot Hose Transportation and Deployment by Distributed Round-Robin Q-Learning

   22 citations · 2015
2. 2
   Transfer learning with Partially Constrained Models: Application to reinforcement learning of linked multicomponent robot system control

   19 citations · 2012
3. 3
   STATE-ACTION VALUE FUNCTION MODELED BY ELM IN REINFORCEMENT LEARNING FOR HOSE CONTROL PROBLEMS

   17 citations · 2013
4. 4
   Undesired state-action prediction in multi-agent reinforcement learning for linked multi-component robotic system control

   14 citations · 2013
5. 5
   Reinforcement Learning endowed with safe veto policies to learn the control of Linked-Multicomponent Robotic Systems

   13 citations · 2015
6. 6
   IMPROVING THE CONTROL OF SINGLE ROBOT HOSE TRANSPORT

   12 citations · 2012
7. 7
   Linked Multicomponent Robotic Systems: Basic Assessment of Linking Element Dynamical Effect

   10 citations · 2010
8. 8
   Empirical Study of Q-Learning Based Elemental Hose Transport Control

   9 citations · 2011
9. 9
   A L-MCRS dynamics approximation by ELM for Reinforcement Learning

   8 citations · 2014
10. 10
    Training Multiagent Systems by Q‐Learning: Approaches and Empirical Results

    7 citations · 2014

Key Collaborators

Contact & Links