Alain Andres Fernandez

Alain Andres Fernandez

Data Scientist at TECNALIA | PhD in Artificial Intelligence (Focus on Reinforcement Learning)

Supervision

TBD

Published:

  • Student: Unai Ruiz González
  • Directors: Alain Andres (TECNALIA), Javier Del Ser (UPV/EHU)
Click here to read the full abstract TBD

A Reinforcement Learning-Based Approach for Vehicle Platoon Route Optimization in Last-Mile Delivery

Published:

  • Original title: “Enfoque basado en Aprendizaje por Refuerzo para la Optimización de Rutas de Vehículos en Pelotón en la Última Milla”
  • Student: Nagore Bravo Julián
  • Directors: Imanol Echeverria (TECNALIA), Alain Andres (TECNALIA), Roberto Santana (UPV/EHU)
Click here to read the full abstract Classic challenges in combinatorial optimization, such as the Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP), have practical applications in planning, logistics, and transportation. Traditionally, these problems have been extensively studied using exact, heuristic, and meta-heuristic methods. However, the issue of generating high-quality solutions in real time persists, as these methods require starting from scratch each time a new problem needs to be solved. This is where the use of Reinforcement Learning (RL) emerges as a promising alternative for solving combinatorial optimization problems in real time. Time is a crucial factor in real-world routing problems, where conditions can change rapidly and solutions must adapt efficiently to these variations.

TBD

Published:

  • Student: Cloe Atxurra Alves
  • Directors: Jon Azpiazu (TECNALIA), Alain Andres (TECNALIA), Aitziber Mancisidor Barinagarrementeria (UPV/EHU)
Click here to read the full abstract TBD

Anomaly Detection in Wind Turbines: Analysis of Data Imbalance and Temporal Effects

Published:

  • Original title: “Detección de anomalías en turbinas eólicas: Análisis del desequilibrio de los datos y los efectos de la temporalidad”
  • Student: Ane San Jose
  • Directors: Uxue Mori (UPV/EHU), Amaia Abanda (TECNALIA), Alain Andres (TECNALIA)
Click here to read the full abstract In engineering and systems monitoring, an anomaly is defined as a rare event. Detecting these anomalies, which deviate significantly from expected or normal system behavior, is crucial for identifying issues in various contexts, including computer systems and industrial processes. This project focuses on detecting anomalies in the operation of wind turbines used for renewable energy generation. Environmental and operational factors can affect turbine performance, leading to occasional anomalies. Early detection is vital for ensuring efficiency and safety, enabling predictive maintenance, optimizing performance, and extending turbine lifespan.

Trajectory Planning for a Robotic Arm Using Reinforcement Learning

Published:

  • Original title: “Planificación de trayectorias de un brazo robótico mediante aprendizaje por refuerzo”
  • Student: Sergio Garcia Ferreira
  • Directors: Alain Andres (TECNALIA), Jon Azpiazu (TECNALIA), Eduardo Zamudio (VIU)
Click here to read the full abstract Reinforcement Learning has brought about a transformation in robotics, thanks to its ability to develop efficient control techniques through autonomous learning. In particular, Reinforcement Learning has proven to be successful in tasks such as reaching objects with robotic arms. In this work, a solution is developed for training this task in simulated environments, and an experimental setup is established to compare the performance of various model-free algorithms. It is demonstrated that PPO achieves the best results, while SAC exhibits instability in environments with Dense rewards. Furthermore, it is concluded that a Sparse reward is sufficient to solve the task in environments with a precision of 5 cm.