15 citations · 30 across the 6 of their papers we have counts for
6 papers · 1 filter
It's a matter of timescale: non-linear utility in successor features and multi-objective planning and learning
Liam P. H. Mertens, Lucas N. Alegre, Florent Delgrange +3
Time is of the essence when dealing with multiple reward signals and non-linear utility. In this paper we argue that the current main approaches in multi-objective RL (SER and ESR)…
Constructing an Optimal Behavior Basis for the Option Keyboard
Lucas N. Alegre, Ana L. C. Bazzan, André Barreto +1
Multi-task reinforcement learning aims to quickly identify solutions for new tasks with minimal or no additional interaction with the environment. Generalized Policy Improvement (G…
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
Shengyi Huang, Quentin Gallouédec, Florian Felten +30
In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms. However, the complete raw data of the learning curv…
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization
Lucas N. Alegre, Ana L. C. Bazzan, Diederik M. Roijers +2
Multi-objective reinforcement learning (MORL) algorithms tackle sequential decision problems where agents may have different preferences over (possibly conflicting) reward function…
Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer
Lucas N. Alegre, Ana L. C. Bazzan, Bruno C. da Silva
In many real-world applications, reinforcement learning (RL) agents might have to solve multiple tasks, each one typically modeled via a reward function. If reward functions are ex…
Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via Online High-Confidence Change-Point Detection
Lucas N. Alegre, Ana L. C. Bazzan, Bruno C. da Silva
Non-stationary environments are challenging for reinforcement learning algorithms. If the state transition and/or reward functions change based on latent factors, the agent is effe…