An information-theoretic approach to curiosity-driven reinforcement learning

It is shown that Boltzmann-style exploration, one of the main exploration methods used in reinforcement learning, is optimal from an information-theoretic point of view, in that it optimally trades expected return for the coding cost of the policy.