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.

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