Description: Horizon-Weighted Returns is a fundamental concept in the field of reinforcement learning, referring to how the rewards obtained by an agent are evaluated and adjusted based on the temporal horizon of its actions. This approach recognizes that decisions made in a dynamic environment should consider not only immediate rewards but also the long-term consequences of those decisions. In this sense, weighted returns allow the agent to assign different levels of importance to rewards received at different times, which can influence its learning strategy. For example, a return that emphasizes future rewards may encourage the agent to adopt behaviors that maximize its long-term performance rather than simply seeking immediate rewards. This concept is crucial for developing more efficient reinforcement learning algorithms, as it allows for better generalization and adaptation to complex environments. In summary, horizon-weighted returns are a tool that helps agents make more informed and strategic decisions, considering both the present and the future in their learning process.
