16 citations · 29 across the 9 of their papers we have counts for
5 papers · 1 filter
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…