Description: The horizon value in the context of reinforcement learning refers to the expected value of future rewards that an agent can obtain over a specific time horizon. This concept is fundamental for decision-making in environments where an agent’s actions not only affect the immediate state but also influence the rewards that will be received in the future. The horizon value allows agents to evaluate not only immediate rewards but also the long-term consequences of their actions. In this sense, it can be seen as a way to anticipate the future and plan accordingly. The choice of time horizon is crucial, as a horizon that is too short may lead to decisions that maximize immediate rewards but are suboptimal in the long run, while a horizon that is too long may cause the agent to become inefficient by not adequately considering immediate rewards. This balance between short-term and long-term rewards is essential for effective learning and strategy optimization in complex environments. In summary, the horizon value is a key component in policy formulation and strategy in reinforcement learning, enabling agents to make more informed and effective decisions.
