Summary of Gta: Generative Trajectory Augmentation with Guidance For Offline Reinforcement Learning, by Jaewoo Lee et al.
GTA: Generative Trajectory Augmentation with Guidance for Offline Reinforcement Learning
by Jaewoo Lee, Sujin Yun, Taeyoung Yun, Jinkyoo Park
First submitted to arxiv on: 27 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 Offline Reinforcement Learning (Offline RL) faces challenges in learning effective decision-making policies from static datasets without online interactions. Data augmentation techniques, such as noise injection and data synthesizing, aim to improve Q-function approximation by smoothing the learned state-action region. However, these methods often fall short of directly improving the quality of offline datasets, leading to suboptimal results. The authors introduce GTA, a novel generative data augmentation approach designed to enrich offline data by augmenting trajectories to be both high-rewarding and dynamically plausible. GTA applies a diffusion model within the data augmentation framework, partially noises original trajectories, and then denoises them with classifier-free guidance via conditioning on amplified return value. The results show that GTA enhances the performance of widely used offline RL algorithms across various tasks with unique challenges. Furthermore, the authors conduct a quality analysis of data augmented by GTA and demonstrate that GTA improves the quality of the data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to make machines learn from old data without actually interacting with the world. Right now, it’s hard for machines to make good decisions based on static data because it doesn’t account for changing circumstances. The authors came up with a new way called GTA (Generative Trajectory Augmentation) that adds more information to the data so it’s more realistic and helps machines make better decisions. They tested GTA on different tasks and showed that it makes the machine learning algorithms perform better. This is important because it can help machines learn from old data in a way that feels more like real life. |
Keywords
» Artificial intelligence » Data augmentation » Diffusion model » Machine learning » Reinforcement learning
