Eureka: Human-Level Reward Design via Coding Large Language Models
Yecheng Jason Ma William Liang Guanzhi Wang De-An Huang Osbert Bastani Dinesh Jayaraman Yuke Zhu Linxi Fan Anima Anandkumar
Large Language Models (LLMs) have excelled as high-level semantic planners
for sequential decision-making tasks. However, harnessing them to learn complex
low-level manipulation tasks, such as dexterous pen spinning, remains an open
problem. We bridge this fundamental gap and present Eureka, a human-level
reward design algorithm powered by LLMs. Eureka exploits the remarkable
zero-shot generation, code-writing, and in-context improvement capabilities of
state-of-the-art LLMs, such as GPT-4, to perform evolutionary optimization over
reward code. The resulting rewards can then be used to acquire complex skills
via reinforcement learning. Without any task-specific prompting or pre-defined
reward templates, Eureka generates reward functions that outperform expert
human-engineered rewards. In a diverse suite of 29 open-source RL environments
that include 10 distinct robot morphologies, Eureka outperforms human experts
on 83% of the tasks, leading to an average normalized improvement of 52%. The
generality of Eureka also enables a new gradient-free in-context learning
approach to reinforcement learning from human feedback (RLHF), readily
incorporating human inputs to improve the quality and the safety of the
generated rewards without model updating. Finally, using Eureka rewards in a
curriculum learning setting, we demonstrate for the first time, a simulated
Shadow Hand capable of performing pen spinning tricks, adeptly manipulating a
pen in circles at rapid speed.