  Training with Reinforcement Learning requires a reward function that is used
to guide the agent towards achieving its objective. However, designing smooth
and well-behaved rewards is in general not trivial and requires significant
human engineering efforts. Generating rewards in self-supervised way, by
inspiring the agent with an intrinsic desire to learn and explore the
environment, might induce more general behaviours. In this work, we propose a
curiosity-based bonus as intrinsic reward for Reinforcement Learning, computed
as the Bayesian surprise with respect to a latent state variable, learnt by
reconstructing fixed random features. We extensively evaluate our model by
measuring the agent's performance in terms of environment exploration, for
continuous tasks, and looking at the game scores achieved, for video games. Our
model is computationally cheap and empirically shows state-of-the-art
performance on several problems. Furthermore, experimenting on an environment
with stochastic actions, our approach emerged to be the most resilient to
simple stochasticity. Further visualization is available on the project
webpage.(https://lbsexploration.github.io/)
