Few-shot In-Context Preference Learning Using Large Language Models: Environment Details by@languagemodels
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Few-shot In-Context Preference Learning Using Large Language Models: Environment Details
by Language ModelsDecember 3rd, 2024
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This section presents environment details for 9 tasks in IsaacGym, including observation and action dimensions, task descriptions, and evaluation metrics. Learn how these elements contribute to preference-based reinforcement learning experiments.
Table of Links
- Abstract and Introduction
- Related Work
- Problem Definition
- Method
- Experiments
- Conclusion and References
A. Appendix
A.1. Full Prompts and A.2 ICPL Details
A.6 Human-in-the-Loop Preference
A.4 ENVIRONMENT DETAILS
In Table 4, we present the observation and action dimensions, along with the task description and task metrics for 9 tasks in IsaacGym.
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]).
This paper is available on arxiv under CC 4.0 license.
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