The Ubiquity and Future of Model-based Reinforcement Learning

As many of you know, I am doing my PhD centered around model-based reinforcement learning (MBRL). This post is not talking about the technical details and recent work, but rather why I am bullish on it for the future. Beyond the prospects of how well it can perform (it's much younger than most of deep RL), having discussions with AI Safety and Ethical AI experts makes it clear that it's more structured learning-setup is pointing towards systems that humans can better understand. Some level of understanding how the system makes decisions is likely a prerequisite for many companies to start using it, else they cannot do real A/B testing and analysis. I will start by showing you the rich set of parallels MBRL has in biological processes, and then show the features making it more suitable for safe deployment in society-facing systems (see why this matters here).