3 papers
cs.AI2025
Enter the Void - Planning to Seek Entropy When Reward is Scarce
Ashish Sundar, Chunbo Luo, Xiaoyang Wang
Model-based reinforcement learning (MBRL) offers an intuitive way to increase the sample efficiency of model-free RL methods by simultaneously training a world model that learns to…
cs.MA2025
Achieving Equilibrium under Utility Heterogeneity: An Agent-Attention Framework for Multi-Agent Multi-Objective Reinforcement Learning
Zhuhui Li, Chunbo Luo, Liming Huang +2
Multi-agent multi-objective systems (MAMOS) have emerged as powerful frameworks for modelling complex decision-making problems across various real-world domains, such as robotic ex…
cs.CV2025
A Sentinel-3 foundation model for ocean colour
Geoffrey Dawson, Remy Vandaele, Andrew Taylor +8
Artificial Intelligence (AI) Foundation models (FMs), pre-trained on massive unlabelled datasets, have the potential to drastically change AI applications in ocean science, where l…