Summary of Large Language Models As Efficient Reward Function Searchers For Custom-environment Multi-objective Reinforcement Learning, by Guanwen Xie et al.
Large Language Models as Efficient Reward Function Searchers for Custom-Environment Multi-Objective Reinforcement Learning
by Guanwen Xie, Jingzehua Xu, Yiyuan Yang, Yimian Ding, Shuai Zhang
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces ERFSL, an efficient reward function searcher using Large Language Models (LLMs), which enables LLMs to effectively search for rewards in complex reinforcement learning tasks. The proposed framework generates reward components based on user requirements and employs a reward critic to identify the correct code form. LLMs then assign weights to these components to balance their values, iteratively adjusting them without ambiguity or redundant adjustments. The framework is applied to an underwater data collection RL task without direct human feedback, achieving zero-shot learning capabilities. The results show that the reward critic successfully corrects the reward code with minimal feedback instances and enables the acquisition of different reward functions within a Pareto solution set. The ERFSL also works well with most prompts utilizing GPT-4o mini, decomposing the weight searching process to reduce numerical and long-context understanding requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn better by giving them clear goals in complex situations. It proposes a new way to design these goal-reward functions using large language models, which are really good at understanding human language. The method generates specific goals based on user needs and adjusts the importance of each goal without getting stuck or doing unnecessary work. The paper shows that this approach works well even when there’s no direct feedback from humans, which is useful for situations where we can’t provide immediate rewards. The results also show that the system can adapt to different scenarios and learn new reward functions quickly. |
Keywords
» Artificial intelligence » Gpt » Reinforcement learning » Zero shot
