Summary of P2dt: Mitigating Forgetting in Task-incremental Learning with Progressive Prompt Decision Transformer, by Zhiyuan Wang et al.
P2DT: Mitigating Forgetting in task-incremental Learning with progressive prompt Decision Transformer
by Zhiyuan Wang, Xiaoyang Qu, Jing Xiao, Bokui Chen, Jianzong Wang
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 As machine learning educators strive to develop intelligent agents capable of adapting to diverse scenarios, a pressing challenge emerges: catastrophic forgetting. This phenomenon causes significant performance degradation when agents are tasked with new problems. To address this issue, researchers propose the Progressive Prompt Decision Transformer (P2DT), a novel solution that enhances transformer-based models by appending decision tokens dynamically during training. By leveraging trajectories collected from traditional reinforcement learning and generating task-specific tokens, P2DT effectively mitigates forgetting in both continual and offline scenarios. Preliminary results demonstrate its ability to alleviate catastrophic forgetting while scaling well with increasing task environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a smart robot that can learn new tricks over time. But sometimes, when it’s faced with a completely new challenge, it forgets what it learned earlier! This is called catastrophic forgetting. To solve this problem, scientists developed a special tool called the Progressive Prompt Decision Transformer (P2DT). It helps robots remember what they learned before and applies it to new tasks. The P2DT works by collecting information from all the things the robot has learned so far and using that knowledge to make better decisions when faced with a new challenge. |
Keywords
* Artificial intelligence * Machine learning * Prompt * Reinforcement learning * Transformer
