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Few-shot In-Context Preference Learning Using Large Language Models: Environment Details

Written by @languagemodels | Published on 2024/12/3
TL;DR —
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.
  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

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.

Table 4: Details of IsaacGym Tasks.

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 (zoeyuchao@gmail.com).

This paper is available on arxiv under CC 4.0 license.

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Written by
@languagemodels
Large Language Models (LLMs) ushered in a technological revolution. We breakdown how the most important models work.
Topics and
tags
reinforcement-learning|in-context-learning|preference-learning|large-language-models|reward-functions|rlhf-efficiency|in-context-preference-learning|human-in-the-loop-rl
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