In this study, the authors explore the significance of reward lookahead in reinforcement learning scenarios. They investigate how agents can benefit from knowing future reward information before taking actions. The research delves into various levels of lookahead, from one-step to full lookahead, and evaluates their impact on maximizing cumulative rewards. By quantifying the competitive ratio between standard RL agents and those with partial future-reward lookahead, the study sheds light on optimizing reward collection strategies. The results reveal insights into worst-case reward distributions, dynamics, and environments that influence agent performance. Additionally, comparisons are drawn with offline RL and reward-free exploration concepts to provide a comprehensive analysis of lookahead strategies in reinforcement learning.
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Önemli Bilgiler Şuradan Elde Edildi
by Nadav Merlis... : arxiv.org 03-19-2024
https://arxiv.org/pdf/2403.11637.pdfDaha Derin Sorular