HPRS: hierarchical potential-based reward shaping from task specifications.

ABSTRACT: The automatic synthesis of policies for robotics systems through reinforcement learning relies upon, and is intimately guided by, a reward signal. Consequently, this signal should faithfully reflect the designer's intentions, which are often expressed as a collection of high-level requirements. Several works have been developing automated reward definitions from formal requirements, but they show limitations in producing a signal which is both effective in training and able to fulfill multiple heterogeneous requirements. In this paper, we define a task as a partially ordered set of safety, target, and comfort requirements and introduce an automated methodology to enforce a natural order among requirements into the reward signal. We perform this by automatically translating the requirements

SUBMITTER: Berducci L 

PROVIDER: S-EPMC11848067 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature