Summary of Continual Task Learning Through Adaptive Policy Self-composition, by Shengchao Hu et al.
Continual Task Learning through Adaptive Policy Self-Composition
by Shengchao Hu, Yuhang Zhou, Ziqing Fan, Jifeng Hu, Li Shen, Ya Zhang, Dacheng Tao
First submitted to arxiv on: 18 Nov 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 The paper tackles a significant challenge in offline reinforcement learning (RL) by developing an agent that can learn from offline trajectories to perform well in a sequence of tasks. The goal is for the agent to rapidly adapt to new tasks while retaining knowledge from previously learned tasks, a requirement for long-lived agents. The authors create a benchmark called Offline Continual World and demonstrate that conventional continual learning (CL) methods struggle with catastrophic forgetting in these scenarios due to distribution shifts. To address this challenge, they introduce CompoFormer, a structure-based continual transformer model that leverages semantic correlations to selectively integrate relevant prior policies alongside newly trained parameters. The results show that CompoFormer outperforms conventional CL methods, particularly in longer task sequences, achieving a promising balance between plasticity and stability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making an AI agent learn new tasks from data without forgetting what it already knows. This is important for agents that need to keep learning over time. The authors created a special test to see how well different methods work in this setting, and they found that most methods don’t do well because the new task is very different from the old ones. To solve this problem, they made a new AI model called CompoFormer that can combine what it knows with new information to learn faster and better. The results show that CompoFormer works much better than other methods, especially when there are many tasks in a row. |
Keywords
» Artificial intelligence » Continual learning » Reinforcement learning » Transformer
