Summary of The Power Of Resets in Online Reinforcement Learning, by Zakaria Mhammedi et al.
The Power of Resets in Online Reinforcement Learning
by Zakaria Mhammedi, Dylan J. Foster, Alexander Rakhlin
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 reinforcement learning protocol called local simulator access enables efficient exploration of high-dimensional domains, leveraging simulators to achieve general function approximation. The approach allows agents to reset to previously observed states and follow their dynamics during training, unlocking new statistical guarantees for online reinforcement learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning uses computer simulations to train artificial intelligence agents. This helps them make good decisions in complex situations. Researchers have developed a way to use these simulators more effectively, especially when the problem is very hard and requires a lot of computation. This method, called local simulator access, allows the agent to go back to previous states it has seen and see how they change over time. This helps the agent learn faster and make better decisions. |
Keywords
» Artificial intelligence » Reinforcement learning
