3 papers
cs.AI2025
Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning
Lynn Cherif, Flemming Kondrup, David Venuto +3
Agents that can autonomously navigate the web through a graphical user interface (GUI) using a unified action space (e.g., mouse and keyboard actions) can require very large amount…
cs.LG2024
Parseval Regularization for Continual Reinforcement Learning
Wesley Chung, Lynn Cherif, David Meger +1
Loss of plasticity, trainability loss, and primacy bias have been identified as issues arising when training deep neural networks on sequences of tasks -- all referring to the incr…
cs.LG2023
Evaluation of Key Spatiotemporal Learners for Print Track Anomaly Classification Using Melt Pool Image Streams
Lynn Cherif, Mutahar Safdar, Guy Lamouche +6
Recent applications of machine learning in metal additive manufacturing (MAM) have demonstrated significant potential in addressing critical barriers to the widespread adoption of…