In a reward-free environment, what is a suitable intrinsic objective for an agent to pursue so that it can learn an optimal task-agnostic exploration policy? In this paper, we argue that the entropy of the state distribution induced by finite-horizon trajectories is a sensible target. Especially, we present a novel and practical policy-search algorithm, Maximum Entropy POLicy optimization (MEPOL), to learn a policy that maximizes a non-parametric, $k$-nearest neighbors estimate of the state distribution entropy. In contrast to known methods, MEPOL is completely model-free as it requires neither to estimate the state distribution of any policy nor to model transition dynamics. Then, we empirically show that MEPOL allows learning a maximum-entropy exploration policy in high-dimensional, continuous-control domains, and how this policy facilitates learning a variety of meaningful reward-based tasks downstream.
翻译:在无报酬环境中,一个代理人追求什么是合适的内在目标,以便学习最佳任务不可知的探索政策?在本文中,我们争论说,由有限顺位轨道引致的国家分布的通缩是一个明智的目标。 特别是,我们提出了一个新颖而实用的政策研究算法 — — 最大通量政治优化(MEPOL ), 以学习一项政策,使非参数、最近邻对州分布酶的估算最大化。 与已知方法相反,MEPOL完全没有模型,因为它既不要求估计任何政策的状况分布,也不要求模拟过渡动态。 然后,我们从经验上表明MEPOL允许在高维、持续控制领域学习最大限度的随机勘探政策,以及这项政策如何促进在下游学习各种有意义的以奖励为基础的任务。