Summary of Hybrid Reinforcement Learning From Offline Observation Alone, by Yuda Song et al.
Hybrid Reinforcement Learning from Offline Observation Alone
by Yuda Song, J. Andrew Bagnell, Aarti Singh
First submitted to arxiv on: 11 Jun 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 This paper tackles the problem of hybrid reinforcement learning, where an agent combines offline data and online interactions to learn optimal policies. The research focuses on the scenario where only state information is available, which is a more practical and abundant setting. The authors show that even with access to offline data, it can be challenging for the agent to match the performance of algorithms that have reset models of the environment. However, under certain assumptions about the quality of the offline data, they propose an algorithm that provably matches the performance of algorithms that use reset models. This study also includes proof-of-concept experiments demonstrating the effectiveness of their approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using a combination of old and new information to help an artificial intelligence agent make good decisions. The researchers looked at how well this works when we only have information about what’s happening, but not about what actions were taken or what rewards were received. They found that even with some help from the past, it can be hard for the AI to do as well as if it had more complete information. However, they came up with a way to make sure the AI does just as well, as long as the old information is pretty reliable. They tested this idea and showed that it actually works in practice. |
Keywords
» Artificial intelligence » Reinforcement learning
