Description: The exploration horizon in the context of reinforcement learning refers to the time frame in which an agent makes decisions and explores its environment. This concept is crucial for understanding how an agent evaluates the consequences of its actions over time. A short exploration horizon may lead to decisions that prioritize immediate rewards, while a longer horizon allows the agent to consider the future repercussions of its actions, fostering a more planned and long-term strategy. The choice of exploration horizon directly affects how the agent learns and adapts, as it determines the balance between exploring new actions and exploiting known actions that have proven effective. In summary, the exploration horizon is a fundamental element that influences the dynamics of reinforcement learning, affecting both the efficiency of learning and the quality of decisions made by the agent in its environment.
