  Typical reinforcement learning (RL) methods show limited applicability for
real-world industrial control problems because industrial systems involve
various constraints and simultaneously require continuous and discrete control.
To overcome these challenges, we devise a novel RL algorithm that enables an
agent to handle a highly constrained action space. This algorithm has two main
features. First, we devise two distance-based Q-value update schemes, incentive
update and penalty update, in a distance-based incentive/penalty update
technique to enable the agent to decide discrete and continuous actions in the
feasible region and to update the value of these types of actions. Second, we
propose a method for defining the penalty cost as a shadow price-weighted
penalty. This approach affords two advantages compared to previous methods to
efficiently induce the agent to not select an infeasible action. We apply our
algorithm to an industrial control problem, microgrid system operation, and the
experimental results demonstrate its superiority.
