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In this paper, we propose an enhanced approach for Rapid Exploration andeXploitation for AI Agents called REX. Existing AutoGPT-style techniques haveinherent limitations, such as a heavy reliance on precise descriptions fordecision-making, and the lack of a systematic approach to leverage try-and-failprocedures akin to traditional Reinforcement Learning (RL). REX introduces anadditional layer of rewards and integrates concepts similar to Upper ConfidenceBound (UCB) scores, leading to more robust and efficient AI agent performance.This approach has the advantage of enabling the utilization of offlinebehaviors from logs and allowing seamless integration with existing foundationmodels while it does not require any model fine-tuning. Through comparativeanalysis with existing methods such as Chain-of-Thoughts(CoT) and Reasoning viAPlanning(RAP), REX-based methods demonstrate comparable performance and, incertain cases, even surpass the results achieved by these existing techniques.Notably, REX-based methods exhibit remarkable reductions in execution time,enhancing their practical applicability across a diverse set of scenarios.

REX: Rapid Exploration and eXploitation for AI Agents