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Deep reinforcement learning (RL) algorithms enable the development of fullyautonomous agents that can interact with the environment. Brain-computerinterface (BCI) systems decipher human implicit brain signals regardless of theexplicit environment. In this study, we integrated deep RL and BCI to improvebeneficial human interventions in autonomous systems and the performance indecoding brain activities by considering environmental factors. Shared autonomywas allowed between the action command decoded from the electroencephalography(EEG) of the human agent and the action generated from the twin delayed DDPG(TD3) agent for a given environment. Our proposed copilot control scheme with afull blocker (Co-FB) significantly outperformed the individual EEG (EEG-NB) orTD3 control. The Co-FB model achieved a higher target approaching score, lowerfailure rate, and lower human workload than the EEG-NB model. The Co-FB controlscheme had a higher invisible target score and level of allowed humanintervention than the TD3 model. We also proposed a disparity d-index toevaluate the effect of contradicting agent decisions on the control accuracyand authority of the copilot model. We found a significant correlation betweenthe control authority of the TD3 agent and the performance improvement of humanEEG classification with respect to the d-index. We also observed that shiftingcontrol authority to the TD3 agent improved performance when BCI decoding wasnot optimal. These findings indicate that the copilot system can effectivelyhandle complex environments and that BCI performance can be improved byconsidering environmental factors. Future work should employ continuous actionspace and different multi-agent approaches to evaluate copilot performance.

Multiagent Copilot Approach for Shared Autonomy between Human EEG and TD3 Deep Reinforcement Learning