Hierarchical Reinforcement Learning for Robots in POMDPs with Option-Observation Initiation Sets
Channel: Artificial Intelligence Lab Brussels - VUB
Date: 2017-11-10

Many robotic tasks can be decomposed into simpler subtasks, like navigating to specific locations, grasping objects or dropping them somewhere. The Options framework, that decomposes a complex Reinforcement Learning problem into simpler sub-tasks (the options), efficiently encodes the hierarchical nature of robotic tasks.

Another challenge is partial observability. In this video, a robot (pretends to) bring objects from two terminals to a central location. Terminals can become empty, but the robot does not see that when at the central location. The robot therefore has to go to a terminal, see whether it is full or empty, go back to the central location, and remember what it has previously seen in order to select where to go now.

Original paper: Denis Steckelmacher et al., "Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation Sets", AAAI 2018,  https://arxiv.org/abs/1708.06551
Narration: Hélène Plisnier
Music: "Ambient Background" by LEXMusic (CC-BY-NC)