Intrinsically motivated artificial agents learn advantageous behavior without
externally-provided rewards. Previously, it was shown that maximizing mutual
information between agent actuators and future states, known as the empowerment
principle, enables unsupervised stabilization of dynamical systems at upright
positions, which is a prototypical intrinsically motivated behavior for upright
standing and walking. This follows from the coincidence between the objective
of stabilization and the objective of empowerment. Unfortunately, sample-based
estimation of this kind of mutual information is challenging. Recently, various
variational lower bounds (VLBs) on empowerment have been proposed as solutions;
however, they are often biased, unstable in training, and have high sample
complexity. In this work, we propose an alternative solution based on a
trainable representation of a dynamical system as a Gaussian channel, which
allows us to efficiently calculate an unbiased estimator of empowerment by
convex optimization. We demonstrate our solution for sample-based unsupervised
stabilization on different dynamical control systems and show the advantages of
our method by comparing it to the existing VLB approaches. Specifically, we
show that our method has a lower sample complexity, is more stable in training,
possesses the essential properties of the empowerment function, and allows
estimation of empowerment from images. Consequently, our method opens a path to
wider and easier adoption of empowerment for various applications.

为了解决通过变分下界 (VLB) 计算确定性经验上均衡值 (EEI) 方法存在的繁琐、高成本等问题，本文采用了基于可训练的高斯信道来构建一种通用的无偏 EM 算法，提出了一种新型方法，能够在不考虑外在奖励的情况下，通过包含每个执行器和未来状态之间的量的交互信息来实现不同控制环境下的稳定性控制，而且该方法能够大大降低采样的复杂性，并展示了该方法的优点。