  The monotonous nature of repetitive cognitive training may cause losing
interest in it and dropping out by older adults. This study introduces an
adaptive technique that enables a Socially Assistive Robot (SAR) to select the
most appropriate actions to maintain the engagement level of older adults while
they play the serious game in cognitive training. The goal is to develop an
adaptation strategy for changing the robot's behaviour that uses reinforcement
learning to encourage the user to remain engaged. A reinforcement learning
algorithm was implemented to determine the most effective adaptation strategy
for the robot's actions, encompassing verbal and nonverbal interactions. The
simulation results demonstrate that the learning algorithm achieved convergence
and offers promising evidence to validate the strategy's effectiveness.
