REX: Rapid Exploration and eXploitation for AI Agents
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866914654916182016 |
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| author | Murthy, Rithesh Heinecke, Shelby Niebles, Juan Carlos Liu, Zhiwei Xue, Le Yao, Weiran Feng, Yihao Chen, Zeyuan Gokul, Akash Arpit, Devansh Xu, Ran Mui, Phil Wang, Huan Xiong, Caiming Savarese, Silvio |
| author_facet | Murthy, Rithesh Heinecke, Shelby Niebles, Juan Carlos Liu, Zhiwei Xue, Le Yao, Weiran Feng, Yihao Chen, Zeyuan Gokul, Akash Arpit, Devansh Xu, Ran Mui, Phil Wang, Huan Xiong, Caiming Savarese, Silvio |
| contents | In this paper, we propose an enhanced approach for Rapid Exploration and eXploitation for AI Agents called REX. Existing AutoGPT-style techniques have inherent limitations, such as a heavy reliance on precise descriptions for decision-making, and the lack of a systematic approach to leverage try-and-fail procedures akin to traditional Reinforcement Learning (RL). REX introduces an additional layer of rewards and integrates concepts similar to Upper Confidence Bound (UCB) scores, leading to more robust and efficient AI agent performance. This approach has the advantage of enabling the utilization of offline behaviors from logs and allowing seamless integration with existing foundation models while it does not require any model fine-tuning. Through comparative analysis with existing methods such as Chain-of-Thoughts(CoT) and Reasoning viA Planning(RAP), REX-based methods demonstrate comparable performance and, in certain 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_08962 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | REX: Rapid Exploration and eXploitation for AI Agents Murthy, Rithesh Heinecke, Shelby Niebles, Juan Carlos Liu, Zhiwei Xue, Le Yao, Weiran Feng, Yihao Chen, Zeyuan Gokul, Akash Arpit, Devansh Xu, Ran Mui, Phil Wang, Huan Xiong, Caiming Savarese, Silvio Artificial Intelligence Machine Learning In this paper, we propose an enhanced approach for Rapid Exploration and eXploitation for AI Agents called REX. Existing AutoGPT-style techniques have inherent limitations, such as a heavy reliance on precise descriptions for decision-making, and the lack of a systematic approach to leverage try-and-fail procedures akin to traditional Reinforcement Learning (RL). REX introduces an additional layer of rewards and integrates concepts similar to Upper Confidence Bound (UCB) scores, leading to more robust and efficient AI agent performance. This approach has the advantage of enabling the utilization of offline behaviors from logs and allowing seamless integration with existing foundation models while it does not require any model fine-tuning. Through comparative analysis with existing methods such as Chain-of-Thoughts(CoT) and Reasoning viA Planning(RAP), REX-based methods demonstrate comparable performance and, in certain 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. |
| title | REX: Rapid Exploration and eXploitation for AI Agents |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2307.08962 |