Universal Notice Networks: Transferring Learned Skills Through a Broad Panel of Applications

Overview & Synopsis

Despite great achievements of reinforcement learning based works, those methods are known for their poor sample eciency. This particular drawback usually means training agents in simulated environments is the only viable option, regarding time constraints. Furthermore, reinforcement learning agents have a strong tendency to overt on their environment, observing a drastic loss of performances at test time. As a result, tying the agent logic to its current body may very well make transfer unefcient. To tackle that issue, we propose the Universal Notice Network (UNN) method to enforce separation of the neural network layers holding information to solve the task from those related to robot properties, hence enabling easier transfer of knowledge between entities. We demonstrate the eciency of this method on a broad panel of applications, we consider dierent kinds of robots, with dierent morphological structures performing kinematic, dynamic single and multi-robot tasks. We prove that our method produces zero shot (without additionnal learning) transfers that may produce better performances than state-of-the art approaches and show that a fast tuning enhances those performances.