  In the Economic Nonlinear Model Predictive (ENMPC) context, closed-loop
stability relates to the existence of a storage function satisfying a
dissipation inequality. Finding the storage function is in general -- for
nonlinear dynamics and cost -- challenging, and has attracted attentions
recently. Q-Learning is a well-known Reinforcement Learning (RL) techniques
that attempts to capture action-value functions based on the state-input
transitions and stage cost of the system. In this paper, we present the use of
the Q-Learning approach to obtain the storage function and verify the
dissipativity for discrete-time systems subject to state-input constraints. We
show that undiscounted Q-learning is able to capture the storage function for
dissipative problems when the parameterization is rich enough. The efficiency
of the proposed method will be illustrated in the different case studies.
