Unsupervised Learning and Exploration of Reachable Outcome Space
Giuseppe Paolo,Alban Laflaquière,Alexandre Coninx,Stephane Doncieux,Giuseppe Paolo,Alban Laflaquière,Alexandre Coninx,Stephane Doncieux
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 TAXONS, a Task Agnostic eXploration of Out...
