Intrinsic interactive reinforcement learning - Using error-related potentials for real world human-robot interaction.

ABSTRACT: Reinforcement learning (RL) enables robots to learn its optimal behavioral strategy in dynamic environments based on feedback. Explicit human feedback during robot RL is advantageous, since an explicit reward function can be easily adapted. However, it is very demanding and tiresome for a human to continuously and explicitly generate feedback. Therefore, the development of implicit approaches is of high relevance. In this paper, we used an error-related potential (ErrP), an event-related activity in the human electroencephalogram (EEG), as an intrinsically generated implicit feedback (rewards) for RL. Initially we validated our approach with seven subjects in a simulated robot learning scenario. ErrPs were detected online in single trial with a balanced accuracy (bACC) of 91%, which was su

SUBMITTER: Kim SK 

PROVIDER: S-EPMC5730605 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature