Summary of Sliding Puzzles Gym: a Scalable Benchmark For State Representation in Visual Reinforcement Learning, by Bryan L. M. De Oliveira et al.
Sliding Puzzles Gym: A Scalable Benchmark for State Representation in Visual Reinforcement Learning
by Bryan L. M. de Oliveira, Murilo L. da Luz, Bruno Brandão, Luana G. B. Martins, Telma W. de L. Soares, Luckeciano C. Melo
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 The abstract presents a challenge in evaluating representation learning separately from policy learning in reinforcement learning (RL) benchmarks. To address this, the authors introduce the Sliding Puzzles Gym (SPGym), a novel benchmark that offers precise control over representation complexity through visual diversity. The SPGym is a classic 8-tile puzzle with a visual observation space of images sourced from arbitrarily large datasets. Despite its apparent simplicity, the task reveals fundamental limitations in current RL algorithms as they struggle to generalize across different visual inputs while maintaining consistent puzzle-solving capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new benchmark called Sliding Puzzles Gym (SPGym) that helps evaluate representation learning separately from policy learning in reinforcement learning. SPGym is a puzzle game with images sourced from large datasets, allowing researchers to test how well algorithms learn to represent visual information. The results show that current algorithms struggle to solve the puzzle when the visual input changes. |
Keywords
* Artificial intelligence * Reinforcement learning * Representation learning
