Abstract: The challenge in managing power grid networks lies not only in dealing with the uncertainty of power demand and generation, or the uncertain events, but also with the huge action space even in a moderately-sized grid. In most such scenarios, the grid operator relies on his/her own experience or at best, some of the potential heuristics whose scope is limited to mitigating only a certain type of uncertainties. The present disclosure provides a heuristic-guided RL framework, for robust control of power networks subjected to production and demand uncertainty, as well as adversarial attacks. Using a careful action selection process, in combination with line reconnection and recovery heuristics, equips the present disclosure to outperform conventional approaches on several challenge datasets even with reduced action space. The present disclosure not only diversifies its actions across substations, but also learns to identify important action sequences to protect the network against targeted adversarial attacks.