Bootstrapping Human-Autonomy Collaborations by using Brain-Computer Interface of SSVEP for Multi-Agent Deep Reinforcement Learning

Human-Autonomy Teaming (HAT) has become one of the emerging AI trends due to the advances in sophisticated machine design that allows closer cooperation with humans while performing moral, reasonable, and applicable tasks as humans’ most exemplary assistants. Based on HAT’s pursuing the collective goal and sharing the authority between humans and machines, our research aims at answering whether humans’ brain-computer interface (BCI) helps achieve efficient collaborations of human with Reinforcement Learning (RL) agents. How can it efficiently facilitate human-in-the-loop guidance to bootstrap the training of the agents? This study proposes a BCI-based system that interacts with RL agents as a human-in-the-loop teaming integration. The neural responses elicited by the Steady-State Visual Evoked Potential in BCI facilitate the collaboration of learning agents with humans and accomplish this goal in a game simulation environment. The results of our proposed system, NeuroRL, show significant improvement by reducing the non-stationarity of exploitations and explorations in the RL agents. With BCI-assisted human-in-the-loop, the rewards can be optimized during the early investigations to achieve more efficient convergence in the training. The novel design proposed in this study can extend the development of the emerging HAT field and knowledge-based RL systems for various applications in dynamic environments.