3 citations · 7 across the 6 of their papers we have counts for
7 papers
Objective-Behavior Alignment: Diagnostics for MORL Policy Selection
Antonio Mone, Zuzanna Osika, Florian Felten +4
Real-world decision-making often requires optimizing multiple competing objectives simultaneously. In reinforcement learning (RL), this is typically addressed by combining reward s…
EngiAI: Capability-Based Evaluation of Tool-Connected LLM Agents for Engineering Design
Gioele Molinari, Florian Felten, Soheyl Massoudi +1
Engineering-agent systems are proliferating, but differences in tasks, tools, and success criteria make demonstrations difficult to compare and failures difficult to diagnose. We i…
EngiBench: A Framework for Data-Driven Engineering Design Research
Florian Felten, Gabriel Apaza, Gerhard Bräunlich +9
Engineering design optimization seeks to automatically determine the shapes, topologies, or parameters of components that maximize performance under given conditions. This process…
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning
Florian Felten, Umut Ucak, Hicham Azmani +10
Many challenging tasks such as managing traffic systems, electricity grids, or supply chains involve complex decision-making processes that must balance multiple conflicting object…
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…
Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework
Florian Felten, El-Ghazali Talbi, Grégoire Danoy
Multi-objective reinforcement learning (MORL) extends traditional RL by seeking policies making different compromises among conflicting objectives. The recent surge of interest in…
Hyperparameter Optimization for Multi-Objective Reinforcement Learning
Florian Felten, Daniel Gareev, El-Ghazali Talbi +1
Reinforcement learning (RL) has emerged as a powerful approach for tackling complex problems. The recent introduction of multi-objective reinforcement learning (MORL) has further e…