Teaching Drones on the Fly: Can Emotional Feedback Serve as Learning Signal for Training Artificial Agents?
This work addresses the challenge of making reinforcement learning more naturalistic and intuitive for non-expert users, though it is incremental as it builds on existing human-in-the-loop methods.
The study tackled the problem of using human emotional feedback as a reward signal for training artificial agents, specifically in an interactive drone training setting, and found that human facial emotion expression can be directly exploited as a reward signal, providing a first empirical proof-of-concept.
We investigate whether naturalistic emotional human feedback can be directly exploited as a reward signal for training artificial agents via interactive human-in-the-loop reinforcement learning. To answer this question, we devise an experimental setting inspired by animal training, in which human test subjects interactively teach an emulated drone agent their desired command-action-mapping by providing emotional feedback on the drone's action selections. We present a first empirical proof-of-concept study and analysis confirming that human facial emotion expression can be directly exploited as reward signal in such interactive learning settings. Thereby, we contribute empirical findings towards more naturalistic and intuitive forms of reinforcement learning especially designed for non-expert users.