4 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-objectiveRL (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…
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…
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…