Explore how human-in-the-loop preferences refine reward functions in tasks like humanoid running and jumping.

Explore how In-Context Preference Learning (ICPL) progressively refined reward functions in humanoid tasks using proxy human preferences.

Discover the key environment details, task descriptions, and metrics for 9 tasks in IsaacGym, as outlined in this paper.

Learn how disagreement sampling and PrefPPO optimize reward learning in reinforcement learning.

Full Prompts and ICPL Details for study Few-shot in-context preference learning with LLMs

ICPL enhances reinforcement learning by integrating LLMs and human preferences for efficient reward function synthesis.

A. Appendix

ICPL integrates LLMs with human preferences to iteratively synthesize reward functions, offering an efficient, feedback-driven approach to RL reward design.

Explore how ICPL builds on foundational works like EUREKA to redefine reward design in reinforcement learning.

Explore how ICPL builds on foundational works like EUREKA to redefine reward design in reinforcement learning.

Discover ICPL, a novel approach that leverages Large Language Models to enhance reward learning efficiency in reinforcement learning.