Summary of Offline Multitask Representation Learning For Reinforcement Learning, by Haque Ishfaq et al.
Offline Multitask Representation Learning for Reinforcement Learning
by Haque Ishfaq, Thanh Nguyen-Tang, Songtao Feng, Raman Arora, Mengdi Wang, Ming Yin, Doina Precup
First submitted to arxiv on: 18 Mar 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 The paper explores offline multitask representation learning in reinforcement learning, where a learner is provided with an offline dataset from various tasks that share a common representation. The authors theoretically investigate offline multitask low-rank RL and propose the MORL algorithm for offline multitask representation learning. They also examine downstream RL in reward-free scenarios, both offline and online, where a new task is introduced to the agent sharing the same representation as upstream offline tasks. The theoretical results highlight the benefits of using the learned representation from the upstream offline task instead of directly learning the representation of the low-rank model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how computers can learn multiple things at once using old data, even if they’re not doing those tasks now. It makes a new way to do this called MORL and shows that it’s better than other ways. They also tested it by introducing a new task to the computer, where it had to use what it learned before to help with the new task. |
Keywords
* Artificial intelligence * Reinforcement learning * Representation learning
