NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:5017
Title:Language as an Abstraction for Hierarchical Deep Reinforcement Learning

The additional experiments presented in the abstract addressed many of the reviewers concerns; however, there was some doubt that these changes will be successfully incorporated into a camera ready. These additions (especially the use of a full language model in the policy and the crafting world results) would significantly strengthen the paper and I strongly urge the authors to follow through on their rebuttal commitment of integrating these results in future revisions. There are also concerns that the approach is highly specialized for the environment and is limited by its need for automatic goal language prediction / verification to perform HIL. Given the content of the paper already, this might be better left to future work.