Description: The discount factor is a fundamental parameter in reinforcement learning that determines the importance of future rewards in decision-making. This value, which ranges from 0 to 1, allows learning agents to prioritize immediate rewards over future ones or vice versa. A discount factor close to 0 means that the agent focuses primarily on immediate rewards, almost completely ignoring rewards that will be received later. On the other hand, a discount factor close to 1 indicates that the agent considers future rewards almost as important as immediate ones, which can lead to more strategic and long-term decisions. The choice of the discount factor is crucial, as it influences the agent’s behavior and its ability to learn from past experiences. An inappropriate value can result in suboptimal learning, where the agent fails to maximize its total reward over time. Therefore, the discount factor not only affects how actions are evaluated but also has a significant impact on the convergence and stability of the learning process. In summary, the discount factor is an essential component that helps agents balance immediate rewards with long-term planning in dynamic and complex environments.
