Summary of Gram: Generalization in Deep Rl with a Robust Adaptation Module, by James Queeney et al.
GRAM: Generalization in Deep RL with a Robust Adaptation Module
by James Queeney, Xiaoyi Cai, Mouhacine Benosman, Jonathan P. How
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO); Machine Learning (stat.ML)
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 Deep reinforcement learning models are often challenged to adapt to new situations outside the ones they were trained for. A key issue is ensuring that these models can generalize well across different conditions, both within and outside their training data. This paper proposes a framework that tackles this challenge by introducing a robust adaptation module that helps identify and respond to changes in the environment’s dynamics. The approach combines two important goals: adapting to familiar scenarios and being robust to unexpected ones. The authors demonstrate the effectiveness of their algorithm, called GRAM, on various simulated tasks involving a quadruped robot. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning robots can learn new things, but they sometimes struggle when things change in ways they didn’t see before. This paper helps solve that problem by creating a special tool that lets robots adapt to changes in the environment. It’s like having a “Plan B” for unexpected situations! The tool is called GRAM and it works really well on simulated robot tasks. |
Keywords
» Artificial intelligence » Deep learning » Reinforcement learning
