Description: The horizon length is a fundamental concept in the field of reinforcement learning and model optimization, referring to the duration during which future outcomes are considered in decision-making processes. This parameter is crucial as it determines how far into the future the consequences of actions taken in a given environment are evaluated. In practical terms, a short horizon length may lead to decisions that maximize immediate rewards but can result in suboptimal long-term performance. Conversely, a longer horizon length allows agents to consider the future repercussions of their actions, promoting more strategic and sustainable decisions. The horizon length can be adjusted based on the problem context, and its choice can significantly influence the model’s behavior and the quality of decisions made. In summary, the horizon length is a determining factor in formulating effective strategies in dynamic and complex environments, where decisions must balance immediate rewards with long-term benefits.
