Learning and Planning with a Semantic Model

arXiv: Learning, Volume abs/1809.10842, 2018.

Cited by: 0|Bibtex|Views76
EI
Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this challenge in the context of man-made environments, which are visually diverse but contain intrinsic semantic regularities. We propose a hybrid model-based and mode...More

Code:

Data:

Full Text
Your rating :
0

 

Tags
Comments