4 papers · 2 filters
Sample Efficient Hierarchical Reinforcement Learning via Best Policy Identification
Anders Jonsson, Emilie Kaufmann, Gianmarco Tedeschi +1
We present HBPI-UCRL, a model-based algorithm for hierarchical reinforcement learning (HRL) that learns high-level and low-level policies in parallel. HBPI-UCRL exploits the fact t…
Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics
Santiago Amaya-Corredor, Miguel Calvo-Fullana, Anders Jonsson
We present a distributed approach for constrained Multi-Agent Reinforcement Learning (MARL) that combines state-augmented policy learning with distributed consensus over dual varia…
The Terminal Representation in Reinforcement Learning
Amir Esterhuysen, Anders Jonsson
Representation learning is a powerful tool for spatio-temporal abstraction within reinforcement learning (RL). Two well established approaches are through the successor representat…
Sampling-guided exploration of active feature selection policies
Gabriel Bernardino, Anders Jonsson, Patrick Clarysse +1
Determining the most appropriate features for machine learning predictive models is challenging regarding performance and feature acquisition costs. In particular, global feature c…