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

Written by languagemodels | Published 2024/12/03
Tech Story Tags: reinforcement-learning | in-context-learning | preference-learning | large-language-models | reward-functions | rlhf-efficiency | in-context-preference-learning | human-in-the-loop-rl

TLDR 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.via the TL;DR App
  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.

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.

Written by languagemodels | Large Language Models (LLMs) ushered in a technological revolution. We breakdown how the most important models work.
Published by HackerNoon on 2024/12/03
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