  We propose a new policy parameterization for representing 3D rotations during
reinforcement learning. Today in the continuous control reinforcement learning
literature, many stochastic policy parameterizations are Gaussian. We argue
that universally applying a Gaussian policy parameterization is not always
desirable for all environments. One such case in particular where this is true
are tasks that involve predicting a 3D rotation output, either in isolation, or
coupled with translation as part of a full 6D pose output. Our proposed Bingham
Policy Parameterization (BPP) models the Bingham distribution and allows for
better rotation (quaternion) prediction over a Gaussian policy parameterization
in a range of reinforcement learning tasks. We evaluate BPP on the rotation
Wahba problem task, as well as a set of vision-based next-best pose robot
manipulation tasks from RLBench. We hope that this paper encourages more
research into developing other policy parameterization that are more suited for
particular environments, rather than always assuming Gaussian.
