Summary of Continual Offline Reinforcement Learning Via Diffusion-based Dual Generative Replay, by Jinmei Liu et al.
Continual Offline Reinforcement Learning via Diffusion-based Dual Generative Replay
by Jinmei Liu, Wenbin Li, Xiangyu Yue, Shilin Zhang, Chunlin Chen, Zhi Wang
First submitted to arxiv on: 16 Apr 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 Continual offline reinforcement learning is a paradigm that enables forward transfer and mitigates catastrophic forgetting to tackle sequential offline tasks. Our dual generative replay framework retains previous knowledge by concurrently replaying generated pseudo-data. We decouple the policy into a diffusion-based generative behavior model and a multi-head action evaluation model, allowing for distributional expressivity and encompassing diverse behaviors. A task-conditioned diffusion model is trained to mimic state distributions of past tasks, generating high-fidelity replayed samples. Experiments demonstrate that our method achieves better forward transfer with less forgetting, approximating results using previous ground-truth data due to its high-fidelity replay. Our code is available at https://github.com/NJU-RL/CuGRO. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn new skills without forgetting old ones. It’s like a person remembering how to ride a bike after years of not riding one. The researchers created a special way for the computer to remember what it learned before, so it can use that knowledge to do better in the future. They tested their method and found that it worked well, allowing the computer to learn new things without forgetting old skills. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Reinforcement learning
