Description: Tactical learning is a form of learning that focuses on short-term decision-making strategies. This approach is based on the idea that agents, whether human or machine, can learn to optimize their decisions through direct experience and immediate feedback. Unlike other learning methods, such as supervised learning, which requires a labeled dataset, tactical learning allows agents to explore their environment and learn from the consequences of their actions. This type of learning is particularly relevant in situations where decisions must be made quickly and where conditions may change unpredictably. Key characteristics of tactical learning include adaptability, the ability to learn from past mistakes, and continuous optimization of strategies. This approach is fundamental in fields such as artificial intelligence, machine learning, and robotics, where systems must interact with dynamic and complex environments. In summary, tactical learning represents an effective methodology for improving real-time decision-making, allowing agents to adjust and evolve based on the information they receive from their environment.
