text:   Learning",6,"['New paper! Not all reinforcement learning problems are suited for a Gaussian policy parameterization!🤯\nPlan to use 3D rotation or 6D pose as part of an action space? Consider the Bingham distribution! 🧵1/5\n\nPaper: <LINK>\nCode: <LINK>\nw/@pabbeel <LINK>', ""Quaternions are often used as the output rotation\nrepresentation when using deep networks, but due to their antipodal symmetric property, sampling a quaternion from a Gaussian doesn't seem appropriate. Here comes the Bingham distribution to the rescue! 🧵2/5 https://t.co/YE6g66omoV"", ""The Bingham distribution is super cool! It's parameterized by an orthogonal 4x4 matrix M, and a diag matrix Z.\nIntuitively, M holds information regarding the *direction* (akin to mean of Gaussian), while Z controls the *spread* (akin to variance) along the direction. 🧵3/5 https://t.co/JJRgKStxgc"", 'Although unexplored for RL, the Bingham distribution has had success in supervised learning! Two notable works are by @valentinp @domo_mr_roboto (https://t.co/EmplIffFRg) and @igilitschenski (https://t.co/YJqrHgu5os). 🧵4/5', 'The gist of our paper is to show *how* we can leverage the power of Bingham for RL! 💪\nWhen evaluating our approach on the Wahba problem and a set of vision-based robot manipulation tasks from RLBench, we achieve superior performance over a Gaussian parameterization! 🧵5/5 https://t.co/OORSIqRa6y', ""@rndmcnlly @pabbeel I'm not familiar with the *matrix* variant you spoke of, but (correct me if I'm wrong), von Mises-Fisher does not model the antipodally symmetric property of Quaternions; I imagine the *matrix* variant would also have this issue. If true, Bingham seems the better option here.""]",22,02,1560