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.

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