Description: Temporal planning is a fundamental process in reinforcement learning that involves decision-making over time to achieve specific goals. This approach is based on the idea that actions taken at a given moment can have consequences that extend over time, requiring careful evaluation of decisions. In this context, reinforcement learning agents must consider not only the immediate reward of an action but also how that action influences future opportunities and the accumulation of long-term rewards. Temporal planning relies on mathematical models and algorithms that allow agents to anticipate the consequences of their actions and optimize their behavior in dynamic environments. This process is essential for solving complex problems where decisions must be strategic and where time plays a crucial role in the effectiveness of actions. Temporal planning is also related to concepts such as the value of information and exploration-exploitation, where agents must balance the search for new strategies with the exploitation of those they already know to maximize their performance. In summary, temporal planning is a key component that enables reinforcement learning systems to adapt and improve their performance in tasks that require a sequence of decisions over time.
