8 citations · 30 across the 8 of their papers we have counts for
8 papers
SQARL: A Size-Agnostic Reinforcement Learning approach for Circuit Allocation in Distributed Quantum Architectures
Víctor Carballo, Júlia López-Closa, Mario Martin
The scaling of quantum processors is currently limited by technical challenges such as decoherence and cross-talk. As the number of qubits grows, interference increases the computa…
Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles
Matteo Gallici, Ivan Masmitja, Mario Martín
Autonomous vehicles (AVs) offer a cost-effective solution for scientific missions such as underwater tracking. Reinforcement learning (RL) has emerged as a powerful method for cont…
Simplifying Deep Temporal Difference Learning
Matteo Gallici, Mattie Fellows, Benjamin Ellis +4
Q-learning played a foundational role in the field reinforcement learning (RL). However, TD algorithms with off-policy data, such as Q-learning, or nonlinear function approximation…
Morphological Symmetries in Robotics
Daniel Ordoñez-Apraez, Giulio Turrisi, Vladimir Kostic +6
We present a comprehensive framework for studying and leveraging morphological symmetries in robotic systems. These are intrinsic properties of the robot's morphology, frequently o…
On discrete symmetries of robotics systems: A group-theoretic and data-driven analysis
Daniel Ordonez-Apraez, Mario Martin, Antonio Agudo +1
We present a comprehensive study on discrete morphological symmetries of dynamical systems, which are commonly observed in biological and artificial locomoting systems, such as leg…
A reinforcement learning path planning approach for range-only underwater target localization with autonomous vehicles
Ivan Masmitja, Mario Martin, Kakani Katija +2
Underwater target localization using range-only and single-beacon (ROSB) techniques with autonomous vehicles has been used recently to improve the limitations of more complex metho…
TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems
Matteo Gallici, Mario Martin, Ivan Masmitja
Coordination is one of the most difficult aspects of multi-agent reinforcement learning (MARL). One reason is that agents normally choose their actions independently of one another…
An Adaptable Approach to Learn Realistic Legged Locomotion without Examples
Daniel Ordonez-Apraez, Antonio Agudo, Francesc Moreno-Noguer +1
Learning controllers that reproduce legged locomotion in nature has been a long-time goal in robotics and computer graphics. While yielding promising results, recent approaches are…