Description: Horizon Discounting is a fundamental concept in reinforcement learning that refers to the practice of reducing the value of future rewards based on their temporal distance. This approach is based on the idea that immediate rewards are generally more valuable than those that will be received in the future, influencing an agent’s decision-making in a learning environment. In technical terms, a discount factor is used, commonly denoted as gamma (γ), which takes values between 0 and 1. A gamma value close to 1 means that the agent considers future rewards almost as valuable as immediate ones, while a value close to 0 makes the agent focus almost exclusively on immediate rewards. This principle is crucial for the stability and convergence of reinforcement learning algorithms, as it helps balance exploration and exploitation. Furthermore, Horizon Discounting allows agents to learn long-term strategies, optimizing their behavior in complex and dynamic environments. In summary, this concept is not only essential for formulating effective policies in reinforcement learning but also reflects the human tendency to value the immediate over the distant.
