Inspired by human conscious planning, we propose Skipper, a model-based
reinforcement learning agent that utilizes spatial and temporal abstractions to
generalize learned skills in novel situations. It automatically decomposes the
task at hand into smaller-scale, more manageable subtasks and hence enables
sparse decision-making and focuses its computation on the relevant parts of the
environment. This relies on the definition of a high-level proxy problem
represented as a directed graph, in which vertices and edges are learned
end-to-end using hindsight. Our theoretical analyses provide performance
guarantees under appropriate assumptions and establish where our approach is
expected to be helpful. Generalization-focused experiments validate Skipper's
significant advantage in zero-shot generalization, compared to existing
state-of-the-art hierarchical planning methods.

Skipper 是一个受人类有意识规划启发的基于模型的强化学习代理，利用空间和时间抽象来在新情境中推广学到的技能，通过自动将任务分解为更小、更可管理的子任务来实现稀疏决策，并将计算集中在环境相关的部分。与现有的基于层次规划的方法相比，基于图的高层代理问题的定义和端到端学习的顶点和边的学习确保了性能保证，理论分析确定了我们的方法在何种情况下有帮助。通过以泛化为重点的实验验证，Skipper 在零样本泛化方面具有显著优势。