16 citations · 29 across the 9 of their papers we have counts for
9 papers
On the Black-box Explainability of Object Detection Models for Safe and Trustworthy Industrial Applications
Alain Andres, Aitor Martinez-Seras, Ibai Laña +1
In the realm of human-machine interaction, artificial intelligence has become a powerful tool for accelerating data modeling tasks. Object detection methods have achieved outstandi…
Fostering Intrinsic Motivation in Reinforcement Learning with Pretrained Foundation Models
Alain Andres, Javier Del Ser
Exploration remains a significant challenge in reinforcement learning, especially in environments where extrinsic rewards are sparse or non-existent. The recent rise of foundation…
On the Inherent Robustness of One-Stage Object Detection against Out-of-Distribution Data
Aitor Martinez-Seras, Javier Del Ser, Aitzol Olivares-Rad +2
Robustness is a fundamental aspect for developing safe and trustworthy models, particularly when they are deployed in the open world. In this work we analyze the inherent capabilit…
Words as Beacons: Guiding RL Agents with High-Level Language Prompts
Unai Ruiz-Gonzalez, Alain Andres, Pedro G. Bascoy +1
Sparse reward environments in reinforcement learning (RL) pose significant challenges for exploration, often leading to inefficient or incomplete learning processes. To tackle this…
Enhanced Generalization through Prioritization and Diversity in Self-Imitation Reinforcement Learning over Procedural Environments with Sparse Rewards
Alain Andres, Daochen Zha, Javier Del Ser
Exploration poses a fundamental challenge in Reinforcement Learning (RL) with sparse rewards, limiting an agent's ability to learn optimal decision-making due to a lack of informat…
Using Offline Data to Speed Up Reinforcement Learning in Procedurally Generated Environments
Alain Andres, Lukas Schäfer, Stefano V. Albrecht +1
One of the key challenges of Reinforcement Learning (RL) is the ability of agents to generalise their learned policy to unseen settings. Moreover, training RL agents requires large…
Towards Improving Exploration in Self-Imitation Learning using Intrinsic Motivation
Alain Andres, Esther Villar-Rodriguez, Javier Del Ser
Reinforcement Learning has emerged as a strong alternative to solve optimization tasks efficiently. The use of these algorithms highly depends on the feedback signals provided by t…
An Evaluation Study of Intrinsic Motivation Techniques applied to Reinforcement Learning over Hard Exploration Environments
Alain Andres, Esther Villar-Rodriguez, Javier Del Ser
In the last few years, the research activity around reinforcement learning tasks formulated over environments with sparse rewards has been especially notable. Among the numerous ap…
Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share?
Alain Andres, Esther Villar-Rodriguez, Javier Del Ser
In the early stages of human life, babies develop their skills by exploring different scenarios motivated by their inherent satisfaction rather than by extrinsic rewards from the e…