Robot reinforcement learning using EEG-based reward signals

Reinforcement learning algorithms have been successfully applied in robotics to learn how to solve tasks based on reward signals obtained during task execution. These reward signals are usually modeled by the programmer or provided by supervision. However, there are situations in which this reward is hard to encode, and so would require a supervised approach of reinforcement learning, where a user directly types the reward on each trial. This paper proposes to use brain activity recorded by an EEG-based BCI system as reward signals. The idea is to obtain the reward from the activity generated while observing the robot solving the task. This process does not require an explicit model of the reward signal. Moreover, it is possible to capture subjective aspects which are specific to each user. To achieve this, we designed a new protocol to use brain activity related to the correct or wrong execution of the task. We showed that it is possible to detect and classify different levels of error in single trials. We also showed that it is possible to apply reinforcement learning algorithms to learn new similar tasks using the rewards obtained from brain activity.