no code implementations • 6 Oct 2019 • Jesse P. Geerts, Kimberly L. Stachenfeld, Neil Burgess
The effectiveness of Reinforcement Learning (RL) depends on an animal's ability to assign credit for rewards to the appropriate preceding stimuli.
no code implementations • 5 Apr 2019 • Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L. Stachenfeld, Pushmeet Kohli, Peter W. Battaglia, Jessica B. Hamrick
Our results show that agents which use structured representations (e. g., objects and scene graphs) and structured policies (e. g., object-centric actions) outperform those which use less structured representations, and generalize better beyond their training when asked to reason about larger scenes.
2 code implementations • ICLR 2019 • David Pfau, Stig Petersen, Ashish Agarwal, David G. T. Barrett, Kimberly L. Stachenfeld
We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization.
no code implementations • NeurIPS 2014 • Kimberly L. Stachenfeld, Matthew Botvinick, Samuel J. Gershman
Furthermore, we demonstrate that this representation of space can support efficient reinforcement learning.
Hierarchical Reinforcement Learning reinforcement-learning +1