3 papers
cs.LG2026
Efficient Exploration Is Enough
Mikel Malagón, Jon Vadillo, Josu Ceberio +2
This work introduces an alternative view of efficient exploration and studies its theoretical and empirical implications in the absence of extrinsic rewards. Specifically, we defin…
cs.LG2025
Self-Composing Policies for Scalable Continual Reinforcement Learning
Mikel Malagón, Josu Ceberio, Jose A. Lozano
This work introduces a growable and modular neural network architecture that naturally avoids catastrophic forgetting and interference in continual reinforcement learning. The stru…
cs.AI2024
Craftium: Bridging Flexibility and Efficiency for Rich 3D Single- and Multi-Agent Environments
Mikel Malagón, Josu Ceberio, Jose A. Lozano
Advances in large models, reinforcement learning, and open-endedness have accelerated progress toward autonomous agents that can learn and interact in the real world. To achieve th…