Combining brain-computer interfaces with deep reinforcement learning for robot training: a feasibility study in a simulation environment.

ABSTRACT: Deep reinforcement learning (RL) is used as a strategy to teach robot agents how to autonomously learn complex tasks. While sparsity is a natural way to define a reward in realistic robot scenarios, it provides poor learning signals for the agent, thus making the design of good reward functions challenging. To overcome this challenge learning from human feedback through an implicit brain-computer interface (BCI) is used. We combined a BCI with deep RL for robot training in a 3-D physical realistic simulation environment. In a first study, we compared the feasibility of different electroencephalography (EEG) systems (wet- vs. dry-based electrodes) and its application for automatic classification of perceived errors during a robot task with different machine learning models. In a second stud

SUBMITTER: Vukelic M 

PROVIDER: S-EPMC10790930 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature