Eureka: Human-Level Reward Design via Coding Large Language Models
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.
6531ddd1a1162a3f87999d40
Yecheng Jason Ma
admin_assigned
2023-10-20T08:32:56.146Z
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Yecheng Ma
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Mark10667
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William Liang
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2023-10-21T09:22:39.778Z
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Will Liang
user
willjhliang
6531ddd1a1162a3f87999d42
Guanzhi Wang
admin_assigned
2023-10-20T08:33:53.375Z
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Guanzhi Wang
user
guanzhi
6531ddd1a1162a3f87999d43
De-An Huang
admin_assigned
2023-10-20T08:34:03.392Z
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De-An Huang
user
deahuang
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Osbert Bastani
6531ddd1a1162a3f87999d45
Dinesh Jayaraman
6531ddd1a1162a3f87999d46
Yuke Zhu
6531ddd1a1162a3f87999d47
Linxi Fan
6531ddd1a1162a3f87999d48
Anima Anandkumar
claimed_verified
2023-10-21T09:22:44.711Z
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Anima Anandkumar
user
animakumar
Eureka: Human-Level Reward Design via Coding Large Language Models
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.
Eureka, an LLM-powered reward design algorithm, generates human-level reward functions enhancing reinforcement learning for complex manipulation tasks and RLHF, including pen spinning with a simulated Shadow Hand.
Large Language Models LLMs zero-shot generation code-writing in-context improvement evolutionary optimization reinforcement learning reward functions RLHF gradient-free in-context learning curriculum learning simulated Shadow Hand