1 citations · 1 across the 1 of their papers we have counts for
5 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…
SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation
Maria Gonzalez-Calabuig, Kai-Hendrik Cohrs, Vishal Nedungadi +7
Geospatial foundation models (GFMs) for Earth observation often fail to perform reliably in environments underrepresented during pretraining. We introduce SHRUG-FM, a framework for…
Exploring Equity of Climate Policies using Multi-Agent Multi-Objective Reinforcement Learning
Palok Biswas, Zuzanna Osika, Isidoro Tamassia +5
Addressing climate change requires coordinated policy efforts of nations worldwide. These efforts are informed by scientific reports, which rely in part on Integrated Assessment Mo…
Multi-Objective Reinforcement Learning for Water Management
Zuzanna Osika, Roxana Rădulescu, Jazmin Zatarain Salazar +2
Many real-world problems (e.g., resource management, autonomous driving, drug discovery) require optimizing multiple, conflicting objectives. Multi-objective reinforcement learning…
Navigating Trade-offs: Policy Summarization for Multi-Objective Reinforcement Learning
Zuzanna Osika, Jazmin Zatarain-Salazar, Frans A. Oliehoek +1
Multi-objective reinforcement learning (MORL) is used to solve problems involving multiple objectives. An MORL agent must make decisions based on the diverse signals provided by di…