LASER: Learning a Latent Action Space for Efficient Reinforcement Learning
Arthur Allshire Roberto Martín-Martín Charles Lin Shawn Manuel Silvio Savarese Animesh Garg Arthur Allshire Roberto Martín-Martín Charles Lin Shawn Manuel Silvio Savarese Animesh Garg
The process of learning a manipulation task depends strongly on the action space used for exploration: posed in the incorrect action space, solving a task with reinforcement learning can be drastically inefficient. Additionally, similar tasks or instances of the same task family impose latent manifold constraints on the most effective action space: the task family can be best solved with actions i...