Summary of Improving Generalization on the Procgen Benchmark with Simple Architectural Changes and Scale, by Andrew Jesson and Yiding Jiang
Improving Generalization on the ProcGen Benchmark with Simple Architectural Changes and Scale
by Andrew Jesson, Yiding Jiang
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper presents a novel approach to improve generalization in reinforcement learning (RL) using recent advances in RL combined with simple architectural changes. The proposed modifications include frame stacking, replacing 2D convolutional layers with 3D convolutional layers, and scaling up the number of convolutional kernels per layer. Experimental results demonstrate a significant reduction in the optimality gap on the ProcGen benchmark, achieving a performance that matches or exceeds current state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers found a way to make AI better at learning new tasks by making small changes to its architecture. They used a special kind of learning called reinforcement learning, which helps the AI learn from rewards and punishments. The team’s improvements allowed the AI to do well on a challenging test called ProcGen, matching or beating other top-performing methods. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning
