Summary of T-dgr: a Trajectory-based Deep Generative Replay Method For Continual Learning in Decision Making, by William Yue et al.
t-DGR: A Trajectory-Based Deep Generative Replay Method for Continual Learning in Decision Making
by William Yue, Bo Liu, Peter Stone
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 Deep generative replay has gained attention for continual learning in decision-making tasks, addressing catastrophic forgetting by generating trajectories from previous experiences. However, existing methods rely on autoregressive models, which accumulate errors. We propose a non-autoregressive approach using a generative model that generates task samples conditioned on trajectory timesteps. Our method achieves state-of-the-art performance on the average success rate metric among continual learning methods in the Continual World benchmarks. By leveraging GitHub (https://github.com/WilliamYue37/t-DGR), we provide an open-source implementation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a computer that can keep learning new things without forgetting what it already knows! This is called “continual learning.” To make this possible, scientists created a way to generate new examples from previous experiences. The problem was that these generated examples had errors that added up over time. We invented a new method that solves this problem and allows the computer to keep learning and improving. Our approach does better than others in tests, showing its potential for real-world applications. |
Keywords
* Artificial intelligence * Attention * Autoregressive * Continual learning * Generative model
