Bridging the Gap: Converting Human Advice into Imagined Examples

By Eric, Yeh. 2019

Subfields: Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. David Krueger
2. Subbarao Kambhampati
3. Tom Everitt
4. Miljan Martic
5. Jan Leike
6. Shane Legg

Citations in the corpus

• Scalable agent alignment via reward modeling: a research direction (2018, background): “Therefore it might be more feasible to provide off-policy feedback on an abstract level, for example using natural language (Yeh et al., 2018).”
• Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation (2020, background): “resort to human explanatory advice in natural language [19, 48].”