learning in reinforced going so far using auxiliary losses and doing things down if I couldn't come down to trying to recover on the line state with a self supervised type loss now there are only ways representation that I can help mainly an exploration which is one of the big challenges in reinforced learning and in Austin for unsupervised feel discovery so let's look at those two now first we can help exploration is through exploration bonuses so what's the idea here in a tabular scenario meaning a very small