Large language models (LLMs) are great for high-level planning, but they need to help master low-level tasks, like turning the pen. However, a team of researchers from NVIDIA, UPenn, Caltech, and UT Austin have developed an algorithm called EUREKA that uses advanced LLMs, such as GPT-4, to create reward functions for the acquisition of complex skills through reinforcement learning. EUREKA outperforms human-designed rewards by providing safer, higher-quality advice through gradient-free in-context learning based on human feedback. This advancement paves the way for the acquisition of LLM-driven skills, as demonstrated by Shadow Hand’s simulated tricks to master pen spinning.
Reward engineering in reinforcement learning has posed challenges as existing methods such as manual trial and error and inverse reinforcement learning need more scalability and adaptability. EUREKA introduces an approach by using LLM to generate interpretable reward codes, improving rewards in real time. While previous work has explored LLMs for decision making, EUREKA is innovative in its application to low-level skill learning tasks, pioneering evolutionary algorithms with LLMs for reward design without initial candidates or indications of low opportunities. .
LLMs excel at high-level planning, but need help with low-level skills, like turning a pen. Reward design in reinforcement learning often relies on time-consuming trial and error. Their study features EUREKA leveraging advanced coding LLMs, such as GPT-4, to autonomously create reward functions for various tasks, outperforming human-designed rewards in various environments. EUREKA also enables in-context learning from human feedback, improving the quality and security of rewards. It addresses the challenge of dexterous manipulation tasks that are unachievable through manual reward engineering.
EUREKA, an LLM-powered algorithm like GPT-4, autonomously generates reward functions, excelling in 29 RL environments. It employs in-context learning from human feedback (RLHF) to improve reward quality and security without model updates. EUREKA rewards allow you to train a simulated Shadow Hand in turning and manipulating the pen quickly. He pioneered evolutionary algorithms with LLM for reward design, eliminating the need for initial candidates or short prompts, marking a significant advance in reinforcement learning.
EUREKA outperforms L2R, showing its expressiveness of reward generation. EUREKA is constantly improving and its best rewards eventually surpass human benchmarks. It creates unique rewards weakly correlated with human ones, potentially revealing counterintuitive design principles. Reflection on reward improves performance on higher-dimensional tasks. Along with curricular learning, EUREKA performs skillful pen-twirling tasks using a simulated Shadow Hand.
EUREKA, an LLM-powered reward design algorithm, achieves human-level reward generation, excelling on 83% of tasks with an average improvement of 52%. Combining LLM with evolutionary algorithms demonstrates a versatile and scalable approach to reward design in open and challenging problems. EUREKA’s success in dexterity is evident in solving complex tasks, such as spinning a pen dexterously, using curricular learning. Its adaptability and substantial performance improvements hold promise for various applications of reinforcement learning and reward design.
Future avenues of research include evaluating the adaptability and performance of EUREKA in more diverse and complex environments and with different robot designs. It is crucial to evaluate its real-world applicability beyond simulation. Exploring synergies with reinforcement learning techniques, such as model-based methods or meta-learning, could further enhance EUREKA’s capabilities. Investigating the interpretability of the reward functions generated by EUREKA is essential to understanding their underlying decision-making processes. Improving the integration of human feedback and exploring the potential of EUREKA in various domains beyond robotics are promising directions.
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Hello, my name is Adnan Hassan. I’m a consulting intern at Marktechpost and soon to be a management trainee at American Express. I am currently pursuing a double degree from the Indian Institute of technology, Kharagpur. I am passionate about technology and I want to create new products that make a difference.
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