15 citations · 30 across the 6 of their papers we have counts for
8 papers
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)…
AMOR: Adaptive Character Control through Multi-Objective Reinforcement Learning
Lucas N. Alegre, Agon Serifi, Ruben Grandia +3
Reinforcement learning (RL) has significantly advanced the control of physics-based and robotic characters that track kinematic reference motion. However, methods typically rely on…
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…
Quantifying the Impact of Non-Stationarity in Reinforcement Learning-Based Traffic Signal Control
Lucas N. Alegre, Ana L. C. Bazzan, Bruno C. da Silva
In reinforcement learning (RL), dealing with non-stationarity is a challenging issue. However, some domains such as traffic optimization are inherently non-stationary. Causes for a…