Interactive Learning from Policy-Dependent Human Feedback

ICML 2017 James MacGlashanMark K HoRobert LoftinBei PengDavid RobertsMatthew E. TaylorMichael L. Littman

For agents and robots to become more useful, they must be able to quickly learn from non-technical users. This paper investigates the problem of interactively learning behaviors communicated by a human teacher using positive and negative feedback... (read more)

PDF Abstract

Code


No code implementations yet. Submit your code now

Tasks


Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet