Unsupervised Learning and Exploration of Reachable Outcome Space

Giuseppe Paolo 1, 2, Alban Laflaqui ere 2, Alexandre Coninx 1 and Stephane Doncieux 1 Abstract -- Performing Reinforcement Learning in sparse rewards settings, with very little prior knowledge, is a challenging problem since there is no signal to properly guide the learning process. In such situations, a good search strategy is fundamental. At the same time, not having to adapt the algorithm to every single problem is very desirable. Here we introduce T AXONS, a T ask Agnostic eXploration of Outcome spaces through Novelty and Surprise algorithm. Based on a population-based divergent-search approach, it learns a set of diverse policies directly from high-dimensional observations, without any task-specific information. T AXONS builds a repertoire of policies while training an autoencoder on the high-dimensional observation of the final state of the system to build a low-dimensional outcome space. The learned outcome space, combined with the reconstruction error, is used to drive the search for new policies. Results show that T AXONS can find a diverse set of controllers, covering a good part of the ground-truth outcome space, while having no information about such space.