Summary of Data Augmentation For Continual Rl Via Adversarial Gradient Episodic Memory, by Sihao Wu et al.
Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory
by Sihao Wu, Xingyu Zhao, Xiaowei Huang
First submitted to arxiv on: 24 Aug 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 This paper investigates the application of data augmentation techniques to Reinforcement Learning (RL) with sequential environments, a crucial component in continual learning. The authors focus on benchmarking various data augmentation methods for continual RL, including existing approaches and a novel technique called Adversarial Augmentation with Gradient Episodic Memory (Adv-GEM). They demonstrate that these augmentations can improve the performance of existing continual RL algorithms, mitigating catastrophic forgetting and promoting forward transfer. Specifically, they show that random amplitude scaling, state-switch, mixup, adversarial augmentation, and Adv-GEM can enhance average performance on robot control tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using clever tricks to make learning machines better at remembering what they learned before. Imagine you’re playing a game where the rules keep changing – you need to learn new things while still keeping old skills. That’s hard! The researchers tried different ways to help the machine remember and found that adding noise or mixing up the information can really improve how well it does. They even invented a new way to do this called Adv-GEM, which is super helpful for learning robots. |
Keywords
» Artificial intelligence » Continual learning » Data augmentation » Reinforcement learning
