1 citations · 1 across the 5 of their papers we have counts for
6 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…
A Behavior-first approach to Multi-Intention Inverse Reinforcement Learning
Antonio Mone, Frans A. Oliehoek, Luciano Cavalcante Siebert
Inverse Reinforcement Learning (IRL) seeks to infer reward functions from expert demonstrations. When demonstrations originate from multiple experts with different intentions, the…
Physics-Informed Reinforcement Learning for Large-Scale EV Smart Charging Considering Distribution Network Voltage Constraints
Stavros Orfanoudakis, Frans A. Oliehoek, Peter Palensky +1
Electric Vehicles (EVs) offer substantial flexibility for grid services, yet large-scale, uncoordinated charging can threaten voltage stability in distribution networks. Existing R…
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…
Task-agnostic Lifelong Robot Learning with Retrieval-based Weighted Local Adaptation
Pengzhi Yang, Xinyu Wang, Ruipeng Zhang +3
A fundamental objective in intelligent robotics is to move towards lifelong learning robot that can learn and adapt to unseen scenarios over time. However, continually learning new…