Description: Horizon optimization is a fundamental concept in reinforcement learning that refers to the process of finding the best strategy or policy to follow over a specific time horizon. This horizon can be finite or infinite, and its choice influences how decisions and actions are evaluated over time. In the context of reinforcement learning, agents must consider not only the immediate reward of their actions but also the future rewards that may arise from those decisions. This involves a balance between exploiting what is already known and exploring new strategies that could be more beneficial in the long run. Horizon optimization is closely related to concepts such as expected value and reward function, where the goal is to maximize the sum of rewards over time. The way the horizon is defined can significantly affect the agent’s behavior, as a shorter horizon may lead to riskier decisions, while a longer horizon may encourage more careful and strategic planning. In summary, horizon optimization is crucial for the development of effective reinforcement learning algorithms, as it enables agents to make informed decisions that maximize their performance in complex and dynamic environments.
