Learning composable policies for environments with complex rules and tasks is a challenging problem. We introduce a hierarchical reinforcement learning framework called the Logical Options Framework (LOF) that learns policies that are satisfying, optimal, and composable. LOF efficiently learns policies that satisfy tasks by representing the task as an automaton and integrating it into learning and planning. We provide and prove conditions under which LOF will learn satisfying, optimal policies. And lastly, we show how LOF's learned policies can be composed to satisfy unseen tasks with only 10-50 retraining steps. We evaluate LOF on four tasks in discrete and continuous domains, including a 3D pick-and-place environment.

中文翻译：

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逻辑选项框架

为具有复杂规则和任务的环境学习可组合策略是一个具有挑战性的问题。我们引入了一种称为逻辑选择框架（LOF）的分层强化学习框架，该框架学习令人满意，最佳和可组合的策略。LOF通过将任务表示为自动机并将其集成到学习和计划中来有效地学习满足任务的策略。我们提供并证明LOF将学习令人满意的最佳政策的条件。最后，我们展示了LOF的学习策略如何仅需10到50个再培训步骤就能满足看不见的任务。我们在离散和连续域（包括3D拾取和放置环境）中的四个任务上评估LOF。