  Fluid administration, also called fluid resuscitation, is a medical treatment
to restore the lost blood volume and optimize cardiac functions in critical
care scenarios such as burn, hemorrhage, and septic shock. Automated fluid
administration systems (AFAS), a potential means to improve the treatment,
employ computational control algorithms to automatically adjust optimal fluid
infusion dosages by targeting physiological variables (e.g., blood volume or
blood pressure). Most of the existing AFAS control algorithms are model-based
approaches, and their performance is highly dependent on the model accuracy,
making them less desirable in real-world care of critically ill patients due to
complexity and variability of modeling patients physiological states. This work
presents a novel model-free reinforcement learning (RL) approach for the
control of fluid infusion dosages in AFAS systems. The proposed RL agent learns
to adjust the blood volume to a desired value by choosing the optimal infusion
dosages using a Q-learning algorithm. The RL agent learns the optimal actions
by interacting with the environment (without having the knowledge of system
dynamics). The proposed methodology (i) overcomes the need for a precise
mathematical model in AFAS systems and (ii) provides a robust performance in
rejecting clinical noises and reaching desired hemodynamic states, as will be
shown by simulation results.
