6 papers
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…
Learning The Minimum Action Distance
Lorenzo Steccanella, Joshua B. Evans, Ãzgür ÅimÅek +1
This paper presents a state representation framework for Markov decision processes (MDPs) that can be learned solely from state trajectories, requiring neither reward signals nor t…
Coordination Graphs for Constrained Multi-Agent Reinforcement Learning
Santiago Amaya-Corredor, Miguel Calvo-Fullana, Anders Jonsson
Constrained Multi-agent reinforcement learning (CMARL) faces two intertwined challenges: the joint action space grows exponentially with the number of agents, and additional requir…
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…
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…
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…