ABSTRACT
Dynamic programming (DP) and reinforcement learning (RL) are algorithmic methods for solving problems in which actions (decisions) are applied to a system over an extended period of time, in order to achieve a desired goal. DP methods require a model of the system’s behavior, whereas RL methods do not. The time variable is usually discrete and actions are taken at every discrete time step, leading to a sequential decision-making problem. The actions are taken in closed loop, which means that the outcome of earlier actions is monitored and taken into account when choosing new actions. Rewards are provided that evaluate the one-step decision-making performance, and the goal is to optimize the long-term performance, measured by the total reward accumulated over the course of interaction.
