Summary of Programmatic Reinforcement Learning: Navigating Gridworlds, by Guruprerana Shabadi et al.
Programmatic Reinforcement Learning: Navigating Gridworlds
by Guruprerana Shabadi, Nathanaël Fijalkow, Théo Matricon
First submitted to arxiv on: 18 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Logic in Computer Science (cs.LO); Programming Languages (cs.PL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers in the field of reinforcement learning (RL) aim to bridge the gap between machine learning and formal methods. They investigate programmatic RL, which involves representing policies as programs that involve higher-order constructs like control loops. The authors focus on a specific class of gridworld environments and define a set of programmatic policies. They then derive upper bounds on the size of optimal programmatic policies and develop an algorithm for synthesizing them. The study contributes to our understanding of programmatic RL, including its theoretical limits and learnability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching computers to make good decisions when they’re not sure what will happen next. It’s a big problem in artificial intelligence, called reinforcement learning (RL). Right now, we don’t know much about how to solve this problem using special kinds of computer programs. The researchers in this study try to answer some basic questions: What are the best ways to make decisions like this? How complex can these decisions be? And how can we teach computers to make good decisions without knowing everything ahead of time? They look at a simple type of problem, called a gridworld, and come up with some ideas about how to solve it. This helps us understand more about teaching computers to make smart decisions. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
