Reflections on NeurIPs 2019

There is a huge push among the researchers here for accountability. I was presenting a poster on "Objective Mismatch in Model-based Reinforcement Learning" at the Deep RL Workshop, and the crowd was very receptive to the idea that some of our underlying assumptions of how RL works may be flawed. I also happened to be presenting my poster next to a researcher at Google pushing for more metrics of reliability in RL algorithms. This means: how consistent is the performance papers propose when they claim a new "state-of-the-art" across environments and random seeds. This realistic robustness may be the key to getting these algorithms to be more useful on real applications (such as robotics which I will always bring up as a great interpretable platform for RL).