1 code implementation • 7 Oct 2020 • Sumedh A. Sontakke, Arash Mehrjou, Laurent Itti, Bernhard Schölkopf
Inspired by this, we attempt to equip reinforcement learning agents with the ability to perform experiments that facilitate a categorization of the rolled-out trajectories, and to subsequently infer the causal factors of the environment in a hierarchical manner.
1 code implementation • 6 Oct 2020 • Sumegh Roychowdhury, Sumedh A. Sontakke, Nikaash Puri, Mausoom Sarkar, Milan Aggarwal, Pinkesh Badjatiya, Balaji Krishnamurthy, Laurent Itti
Also, they are believed to be arranged hierarchically, allowing for an efficient representation of complex long-horizon experiences.