Summary of Synthesizing Programmatic Reinforcement Learning Policies with Large Language Model Guided Search, by Max Liu et al.
Synthesizing Programmatic Reinforcement Learning Policies with Large Language Model Guided Search
by Max Liu, Chan-Hung Yu, Wei-Hsu Lee, Cheng-Wei Hung, Yen-Chun Chen, Shao-Hua Sun
First submitted to arxiv on: 26 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Programming Languages (cs.PL)
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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 Programmatic Reinforcement Learning (PRL) seeks to represent policies through programs for improved interpretability and generalization. Despite promising outcomes, current state-of-the-art PRL methods struggle with sample inefficiency, requiring tens of millions of interactions between programs and environments. To address this challenge, researchers introduce a novel LLM-guided search framework (LLM-GS). By leveraging Large Language Models’ (LLMs) programming expertise and common sense reasoning, LLM-GS enhances the efficiency of assumption-free, random-guessing search methods. The Pythonic-DSL strategy is proposed to generate programs in domain-specific languages (DSLs), initially generating Python codes before converting them into DSL programs. To optimize these generated programs, Scheduled Hill Climbing is developed to efficiently explore the programmatic search space. Experimental results in the Karel domain demonstrate LLM-GS’s superior effectiveness and efficiency. Ablation studies verify the critical role of the Pythonic-DSL strategy and Scheduled Hill Climbing algorithm. Keywords: Programmatic Reinforcement Learning, PRL, Large Language Models, LLMs, domain-specific languages, DSLs, Karel domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to teach computers to make good decisions. Right now, it takes a lot of practice for computers to learn how to do this. To fix this problem, scientists created a new system that uses special language models to help the computer learn faster. This system is called LLM-GS and it works by generating code in a special language that the computer can understand. Then, it helps the computer improve its decisions by trying different options and seeing what works best. The scientists tested this system on a game called Karel and found that it worked much better than before. This means that people who don’t know how to program computers can use this system to make good decisions too! |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning
