Description: Horizon-dependent rewards are a fundamental concept in the field of reinforcement learning, where the rewards received by an agent vary based on the temporal horizon considered. This means that the value of a reward can change depending on how far in the future it is expected to be received. In this context, the temporal horizon refers to the number of steps or decisions the agent must take before receiving a reward. This approach is crucial for decision-making, as it allows agents to evaluate not only immediate rewards but also future ones, influencing their behavior and learning strategies. Short-term rewards may be attractive, but often long-term rewards are more beneficial. Therefore, an agent optimizing its behavior must balance these rewards based on its temporal horizon. This concept is also related to the idea of ‘reward discounting,’ where future rewards are weighted less than immediate ones, reflecting the uncertainty and risk associated with time. In summary, horizon-dependent rewards are essential for developing reinforcement learning algorithms that seek to maximize performance over time, enabling agents to learn more effectively in complex and dynamic environments.
