TY - JOUR AU - Bacon, Pierre-Luc AU - Precup, Doina PY - 2018/03/27 Y2 - 2024/03/29 TI - Constructing Temporal Abstractions Autonomously in Reinforcement Learning JF - AI Magazine JA - AIMag VL - 39 IS - 1 SE - Articles DO - 10.1609/aimag.v39i1.2780 UR - https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/2780 SP - 39-50 AB - The idea of temporal abstraction, i.e. learning, planning and representing the world at multiple time scales, has been a constant thread in AI research, spanning sub-fields from classical planning and search to control and reinforcement learning. For example, programming a robot typically involves making decisions over a set of controllers, rather than working at the level of motor torques. While temporal abstraction is a very natural concept, learning such abstractions with no human input has proved quite daunting. In this paper, we present a general architecture, called option-critic, which allows learning temporal abstractions automatically, end-to-end, simply from the agent’s experience. This approach allows continual learning and provides interesting qualitative and quantitative results in several tasks. ER -