How Do We Teach Reinforcement Learning Agents Human Preferences?

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

Our goal is to design a reward function that can be used to train reinforcement learning agents that demonstrate human-preferred behaviors. It is usually hard to design proper reward functions in reinforcement learning that induce policies that align well with human preferences.

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:::info Authors:

(1) Chao Yu, Tsinghua University;

(2) Hong Lu, Tsinghua University;

(3) Jiaxuan Gao, Tsinghua University;

(4) Qixin Tan, Tsinghua University;

(5) Xinting Yang, Tsinghua University;

(6) Yu Wang, with equal advising from Tsinghua University;

(7) Yi Wu, with equal advising from Tsinghua University and the Shanghai Qi Zhi Institute;

(8) Eugene Vinitsky, with equal advising from New York University ([email protected]).

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:::info This paper is available on arxiv under CC 4.0 license.

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