Goal-conditioned planning benefits from learned low-dimensional
representations of rich, high-dimensional observations. While compact latent
representations, typically learned from variational autoencoders or inverse
dynamics, enable goal-conditioned planning they ignore state affordances, thus
hampering their sample-efficient planning capabilities. In this paper, we learn
a representation that associates reachable states together for effective onward
planning. We first learn a latent representation with multi-step inverse
dynamics (to remove distracting information); and then transform this
representation to associate reachable states together in $\ell_2$ space. Our
proposals are rigorously tested in various simulation testbeds. Numerical
results in reward-based and reward-free settings show significant improvements
in sampling efficiency, and yields layered state abstractions that enable
computationally efficient hierarchical planning.

我们学习到一种能够关联可达状态的表示方法，通过学习多步逆动力学获得一个潜在表示，并将其转换为在 l2 空间中关联可达状态，这种方法可以显著提高采样效率，并实现层次化规划。