“…Such an approach will significantly reduce computational costs, because a stochactic optimization problem is solved offline to find the optimal policy for all possible scenarios. In recent years, several successful studies have been published on the use of advanced RL methods for optimal control of microgrids based on deep Q-networks (DQN) [55,56], Monte-Carlo tree search (MCTS) [57], deep policy gradient [58], batch RL [59], multi-agent RL [60], etc. Part of the research is devoted to comparing the effectiveness of the RL methods (capable of giving quick, but approximate solutions) with traditional optimization methods, for example, mixed-integer linear programming (MILP) [61,62].…”