  Exploration in high-dimensional, continuous spaces with sparse rewards is an
open problem in reinforcement learning. Artificial curiosity algorithms address
this by creating rewards that lead to exploration. Given a reinforcement
learning algorithm capable of maximizing rewards, the problem reduces to
finding an optimization objective consistent with exploration. Maximum entropy
exploration uses the entropy of the state visitation distribution as such an
objective. However, efficiently estimating the entropy of the state visitation
distribution is challenging in high-dimensional, continuous spaces. We
introduce an artificial curiosity algorithm based on lower bounding an
approximation to the entropy of the state visitation distribution. The bound
relies on a result we prove for non-parametric density estimation in arbitrary
dimensions using k-means. We show that our approach is both computationally
efficient and competitive on benchmarks for exploration in high-dimensional,
continuous spaces, especially on tasks where reinforcement learning algorithms
are unable to find rewards.

Machine Learning