12 citations · 14 across the 3 of their papers we have counts for
3 papers
GuideX: Guided Synthetic Data Generation for Zero-Shot Information Extraction
Neil De La Fuente, Oscar Sainz, Iker García-Ferrero +1
Information Extraction (IE) systems are traditionally domain-specific, requiring costly adaptation that involves expert schema design, data annotation, and model training. While La…
Game Theory and Multi-Agent Reinforcement Learning : From Nash Equilibria to Evolutionary Dynamics
Neil De La Fuente, Miquel Noguer i Alonso, Guim Casadellà
This paper explores advanced topics in complex multi-agent systems building upon our previous work. We examine four fundamental challenges in Multi-Agent Reinforcement Learning (MA…
A Comparative Study of Deep Reinforcement Learning Models: DQN vs PPO vs A2C
Neil De La Fuente, Daniel A. Vidal Guerra
This study conducts a comparative analysis of three advanced Deep Reinforcement Learning models: Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Advantage Actor-Crit…