Intelligent robotics policies have been widely researched for challenging applications such as opening doors, washing dishes, and table organization. We refer to a "Policy Pool", containing skills that be easily accessed and reused. There are researches to leverage the pool, such as policy reuse, modular learning, assembly learning, transfer learning, hierarchical reinforcement learning (HRL), etc. However, most methods generally do not perform well in learning efficiency and require large datasets for training. This work focuses on enabling fast learning based on the policy pool. It should learn fast enough in one-shot or few-shot by avoiding learning from scratch. We also allow it to interact and learn from humans, but the training period should be within minutes. We propose FIRL, Fast (one-shot) Imitation, and Policy Reuse Learning. Instead of learning a new skill from scratch, it performs the one-shot imitation learning on the higher layer under a 2-layer hierarchical mechanism. Our method reduces a complex task learning to a simple regression problem that it could solve in a few offline iterations. The agent could have a good command of a new task given a one-shot demonstration. We demonstrate this method on the OpenDoors mini-grid environment, and the code is available on http://www.github.com/yiwc/firl.

通过引入Policy Pool和异步的非条件policy优化策略, 本文提出了一种在机器人与人类之间合作中使用的人类向机器人知识迁移算法, 在 Mini-Grid 环境中对复杂问题仅需要一次人类示范便可快速学习，展示了其非常高的效率和实用性。

适用于快速从人类导师中学习的多才智能