From the 1 of 4 linked papers with an AI index.
4 papers
Training on Irrelevant States Implies Data Augmentation: Generalization in Contextual MDPs
Max Weltevrede, Caroline Horsch, Matthijs T. J. Spaan +1
The paper shows that training reinforcement‑learning agents on additional, irrelevant states acts like data augmentation and can improve zero‑shot generalization in contextual MDPs…
Shared Modular Recurrence in Contextual MDPs for Universal Morphology Control
Laurens Engwegen, Max Weltevrede, Caroline Horsch +2
A universal controller for any robot morphology would greatly improve computational and data efficiency. Steps have been made towards such multi-robot control by utilizing contextu…
Sparse Masked Attention Policies for Reliable Generalization
Caroline Horsch, Laurens Engwegen, Max Weltevrede +2
In reinforcement learning, abstraction methods that remove unnecessary information from the observation are commonly used to learn policies which generalize better to unseen tasks.…
Universal Value-Function Uncertainties
Moritz A. Zanger, Max Weltevrede, Yaniv Oren +4
Estimating epistemic uncertainty in value functions is a crucial challenge for many aspects of reinforcement learning (RL), including efficient exploration, safe decision-making, a…