How ICPL Enhances Reward Function Efficiency and Tackles Complex RL Tasks

Table of Links

1. Abstract and Introduction
2. Related Work
3. Problem Definition
4. Method
5. Experiments
6. Conclusion and References

A. Appendix

A.1. Full Prompts and A.2 ICPL Details

A. 3 Baseline Details

A.4 Environment Details

A.5 Proxy Human Preference

A.6 Human-in-the-Loop Preference

6 CONCLUSION

Our proposed method, In-Context Preference Learning (ICPL), demonstrates significant potential for addressing the challenges of preference learning tasks through the integration of large language models. By leveraging the generative capabilities of LLMs to autonomously produce reward functions, and iteratively refining them using human feedback, ICPL reduces the complexity and human effort typically associated with preference-based RL. Our experimental results, both in proxy human and human-in-the-loop settings, show that ICPL not only surpasses traditional RLHF in efficiency but also competes effectively with methods utilizing ground-truth rewards instead of preferences. Furthermore, the success of ICPL in complex, subjective tasks like humanoid jumping highlights its versatility in capturing nuanced human intentions, opening new possibilities for future applications in complex real-world scenarios where traditional reward functions are difficult to define.

Limitations. While ICPL demonstrates significant potential, it faces limitations in tasks where human evaluators struggle to assess performance from video alone, such as Anymal’s "follow random commands." In such cases, subjective human preferences may not provide adequate guidance. Future work will explore integrating human preferences with artificially designed metrics to enhance the ease with which humans can assess the videos, ensuring more reliable performance in complex tasks. Additionally, we observe that the performance of the task is qualitatively dependent on the diversity of the initial reward functions that seed the search. While we do not study methods to achieve this here, relying on the LLM to provide this initial diversity is a current limitation.

REFERENCES

Saurabh Arora and Prashant Doshi. A survey of inverse reinforcement learning: Challenges, methods and progress. Artificial Intelligence, 297:103500, 2021.

erena Booth, W. Bradley Knox, Julie Shah, Scott Niekum, Peter Stone, and Alessandro Allievi. The perils of trial-and-error reward design: Misdesign through overfitting and invalid task specifications. In Brian Williams, Yiling Chen, and Jennifer Neville (eds.), Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, IAAI 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2023, Washington, DC, USA, February 7-14, 2023, pp. 5920–5929. AAAI Press, 2023. doi: 10.1609/AAAI.V37I5.25733. URL https://doi.org/10.1609/aaai. v37i5.25733.

Banghao Chen, Zhaofeng Zhang, Nicolas Langrené, and Shengxin Zhu. Unleashing the potential of prompt engineering in large language models: a comprehensive review. arXiv preprint arXiv:2310.14735, 2023.

Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep reinforcement learning from human preferences. Advances in neural information processing systems, 30, 2017.

Yuqing Du, Ksenia Konyushkova, Misha Denil, Akhil Raju, Jessica Landon, Felix Hill, Nando de Freitas, and Serkan Cabi. Vision-language models as success detectors. arXiv preprint arXiv:2303.07280, 2023.

Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, and Anima Anandkumar. Minedojo: Building open-ended embodied agents with internet-scale knowledge. Advances in Neural Information Processing Systems, 35: 18343–18362, 2022.

Louie Giray. Prompt engineering with chatgpt: a guide for academic writers. Annals of biomedical engineering, 51(12):2629–2633, 2023.

Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, and Dario Amodei. Reward learning from human preferences and demonstrations in atari. Advances in neural information processing systems, 31, 2018.

Hong Jun Jeon, Smitha Milli, and Anca Dragan. Reward-rational (implicit) choice: A unifying formalism for reward learning. Advances in Neural Information Processing Systems, 33:4415– 4426, 2020.

Siddharth Karamcheti, Suraj Nair, Annie S Chen, Thomas Kollar, Chelsea Finn, Dorsa Sadigh, and Percy Liang. Language-driven representation learning for robotics. arXiv preprint arXiv:2302.12766, 2023.

Minae Kwon, Sang Michael Xie, Kalesha Bullard, and Dorsa Sadigh. Reward design with language models. arXiv preprint arXiv:2303.00001, 2023.

Kimin Lee, Laura Smith, Anca Dragan, and Pieter Abbeel. B-pref: Benchmarking preference-based reinforcement learning. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1).

Kimin Lee, Laura Smith, and Pieter Abbeel. Pebble: Feedback-efficient interactive reinforcement learning via relabeling experience and unsupervised pre-training. arXiv preprint arXiv:2106.05091, 2021a.

Kimin Lee, Laura Smith, Anca Dragan, and Pieter Abbeel. B-pref: Benchmarking preference-based reinforcement learning. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1), 2021b. URL https://openreview.net/forum? id=ps95-mkHF_.

Fei Liu et al. Learning to summarize from human feedback. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020.

Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, and Amy Zhang. Vip: Towards universal visual reward and representation via value-implicit pre-training. arXiv preprint arXiv:2210.00030, 2022.

Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Eureka: Human-level reward design via coding large language models. arXiv preprint arXiv:2310.12931, 2023.

Yecheng Jason Ma, William Liang, Hung-Ju Wang, Sam Wang, Yuke Zhu, Linxi Fan, Osbert Bastani, and Dinesh Jayaraman. Dreureka: Language model guided sim-to-real transfer. arXiv preprint arXiv:2406.01967, 2024.

Eduardo Mosqueira-Rey, Elena Hernández-Pereira, David Alonso-Ríos, José Bobes-Bascarán, and Ángel Fernández-Leal. Human-in-the-loop machine learning: a state of the art. Artificial Intelligence Review, 56(4):3005–3054, 2023.

Soroush Nasiriany, Fei Xia, Wenhao Yu, Ted Xiao, Jacky Liang, Ishita Dasgupta, Annie Xie, Danny Driess, Ayzaan Wahid, Zhuo Xu, et al. Pivot: Iterative visual prompting elicits actionable knowledge for vlms. arXiv preprint arXiv:2402.07872, 2024.

Andrew Y Ng, Stuart Russell, et al. Algorithms for inverse reinforcement learning. In Icml, volume 1, pp. 2, 2000.

Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35: 27730–27744, 2022.

Zhenghao Mark Peng, Wenjie Mo, Chenda Duan, Quanyi Li, and Bolei Zhou. Learning from active human involvement through proxy value propagation. Advances in neural information processing systems, 36, 2024.

Carl Orge Retzlaff, Srijita Das, Christabel Wayllace, Payam Mousavi, Mohammad Afshari, Tianpei Yang, Anna Saranti, Alessa Angerschmid, Matthew E Taylor, and Andreas Holzinger. Human-inthe-loop reinforcement learning: A survey and position on requirements, challenges, and opportunities. Journal of Artificial Intelligence Research, 79:359–415, 2024.

John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms, 2017. URL https://arxiv.org/abs/1707.06347.

Satinder Singh, Richard L Lewis, and Andrew G Barto. Where do rewards come from. In Proceedings of the annual conference of the cognitive science society, pp. 2601–2606. Cognitive Science Society, 2009.

Richard S Sutton. Reinforcement learning: An introduction. A Bradford Book, 2018.

Yufei Wang, Zhanyi Sun, Jesse Zhang, Zhou Xian, Erdem Biyik, David Held, and Zackory Erickson. Rl-vlm-f: Reinforcement learning from vision language foundation model feedback. arXiv preprint arXiv:2402.03681, 2024.

Jules White, Quchen Fu, Sam Hays, Michael Sandborn, Carlos Olea, Henry Gilbert, Ashraf Elnashar, Jesse Spencer-Smith, and Douglas C Schmidt. A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382, 2023.

Christian Wirth, Riad Akrour, Gerhard Neumann, and Johannes Fürnkranz. A survey of preferencebased reinforcement learning methods. Journal of Machine Learning Research, 18(136):1–46, 2017.

Jeff Wu, Long Ouyang, Daniel M Ziegler, Nisan Stiennon, Ryan Lowe, Jan Leike, and Paul Christiano. Recursively summarizing books with human feedback. arXiv preprint arXiv:2109.10862, 2021.

Wenhao Yu, Nimrod Gileadi, Chuyuan Fu, Sean Kirmani, Kuang-Huei Lee, Montserrat Gonzalez Arenas, Hao-Tien Lewis Chiang, Tom Erez, Leonard Hasenclever, Jan Humplik, Brian Ichter, Ted Xiao, Peng Xu, Andy Zeng, Tingnan Zhang, Nicolas Heess, Dorsa Sadigh, Jie Tan, Yuval Tassa, and Fei Xia. Language to rewards for robotic skill synthesis. In Jie Tan, Marc Toussaint, and Kourosh Darvish (eds.), Conference on Robot Learning, CoRL 2023, 6-9 November 2023, Atlanta, GA, USA, volume 229 of Proceedings of Machine Learning Research, pp. 374–404. PMLR, 2023. URL https://proceedings.mlr.press/v229/yu23a.html.