Summary of Ensemble Successor Representations For Task Generalization in Offline-to-online Reinforcement Learning, by Changhong Wang et al.
Ensemble Successor Representations for Task Generalization in Offline-to-Online Reinforcement Learning
by Changhong Wang, Xudong Yu, Chenjia Bai, Qiaosheng Zhang, Zhen Wang
First submitted to arxiv on: 12 May 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 proposes a novel approach to offline-to-online reinforcement learning, addressing the task generalization problem in adapting an initialized offline policy to new tasks through online interactions. The existing methods primarily focus on refining the same task online and neglect the importance of generalizing to different tasks. The authors draw inspiration from successor representations for task generalization in online RL and extend this framework to incorporate offline-to-online learning. They introduce a novel methodology that leverages offline data to acquire an ensemble of successor representations, enabling robust representation learning and facilitating fast adaptation of Q functions towards new tasks during the online fine-tuning phase. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper’s main idea is to use offline data to learn about different tasks and then adapt this knowledge to new tasks through online interactions. The authors show that previous methods are not very good at generalizing to new tasks, but their approach can improve performance by using an ensemble of successor representations learned from offline data. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Online learning » Reinforcement learning » Representation learning
