Description: Value-Based Learning (VBL) is a learning approach that focuses on maximizing the value of actions taken in a given state. This method is based on the idea that decisions should be evaluated not only by their immediate outcomes but also by the long-term value they can generate. In the context of artificial intelligence, VBL is used to train models that can learn to make optimal decisions in complex environments. Through simulation, algorithms can explore different strategies and assess their consequences, allowing for continuous improvement in decision-making. This approach is particularly relevant in areas where decisions have a significant impact, such as robotics, economics, and resource management. The main characteristics of VBL include evaluating actions based on their expected value, adapting to changing environments, and learning from experience. In summary, Value-Based Learning is a powerful tool that enables artificial intelligence systems to optimize their performance and adapt to dynamic situations.
