  We show that reinforcement learning agents that learn by surprise (surprisal)
get stuck at abrupt environmental transition boundaries because these
transitions are difficult to learn. We propose a counter-intuitive solution
that we call Mutual Information Minimising Exploration (MIME) where an agent
learns a latent representation of the environment without trying to predict the
future states. We show that our agent performs significantly better over sharp
transition boundaries while matching the performance of surprisal driven agents
elsewhere. In particular, we show state-of-the-art performance on difficult
learning games such as Gravitar, Montezuma's Revenge and Doom.

Machine Learning;Machine Learning