Summary of Data-incremental Continual Offline Reinforcement Learning, by Sibo Gai and Donglin Wang
Data-Incremental Continual Offline Reinforcement Learning
by Sibo Gai, Donglin Wang
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel continual learning setting is proposed, where an agent learns a sequence of datasets for a single offline reinforcement learning task, introducing the challenge of active forgetting due to conservative learning. This setting is distinct from traditional offline RL tasks with separate datasets. To address this issue, a new algorithm called EREIQL is introduced, utilizing multiple value networks and experience replay to relieve catastrophic forgetting. The proposed algorithm demonstrates effective relief of active forgetting in DICORL and performs well. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists are trying to make computers learn from their experiences better. They’re proposing a new way for computers to learn, where they learn from one task over time, rather than switching between different tasks. This is challenging because the computer will forget some of what it learned earlier if it’s not important for the current task. To overcome this challenge, researchers developed a new algorithm that helps the computer remember what it learned earlier and apply it to new situations. The results show that this approach works well. |
Keywords
» Artificial intelligence » Continual learning » Reinforcement learning
