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The concept of reward is fundamental in reinforcement learning with a wide range of applications in natural and social sciences. Seeking an interpretable reward for decision-making that largely shapes the system's behavior has always been a challenge in reinforcement learning. In this work, we explore a discrete-time reward for reinforcement learning in continuous time and action spaces that represent many phenomena captured by applying physical laws. We find that the discrete-time reward leads to the extraction of the unique continuous-time decision law and improved computational efficiency by dropping the integrator operator that appears in classical results with integral rewards. We apply this finding to solve output-feedback design problems in power systems. The results reveal that our approach removes an intermediate stage of identifying dynamical models. Our work suggests that the discrete-time reward is efficient in search of the desired decision law, which provides a computational tool to understand and modify the behavior of large-scale engineering systems using the optimal learned decision.