Raymond Chua - Predictive Representations and Memory for Continual Reinforcement Learning

​Biological agents, such as humans and animals, are capable of continuous adaptation and learning in complex, dynamic environments. Despite recent advancements in Artificial Intelligence (AI), enabling continual learning remains a major challenge for deep Reinforcement Learning (RL) agents. Understanding the computational principles that support lifelong learning therefore represents both a neuroscience question and an AI challenge. In this talk, I will present two pieces of work from my PhD that address this question. First, I will introduce predictive state representations through Simple Su