Summary of In-context Exploration-exploitation For Reinforcement Learning, by Zhenwen Dai et al.
In-context Exploration-Exploitation for Reinforcement Learning
by Zhenwen Dai, Federico Tomasi, Sina Ghiassian
First submitted to arxiv on: 11 Mar 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 This paper proposes an In-context Exploration-Exploitation (ICEE) algorithm for optimizing the efficiency of online policy learning. By performing exploration-exploitation trade-offs within a Transformer model at inference time, ICEE can learn to solve new reinforcement learning tasks using only tens of episodes, outperforming previous methods that require hundreds of episodes. The authors address the computational costs associated with gathering large training trajectory sets and training large Transformer models by introducing an in-context learning approach. This method achieves efficiency comparable to Gaussian process biased methods but requires significantly less time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The ICEE algorithm can learn to solve new RL tasks using just a few episodes, making it a promising approach for online policy learning of offline reinforcement learning methods. The authors demonstrate the effectiveness of this method in grid world environments and show that it can be used to optimize Bayesian optimization problems. |
Keywords
* Artificial intelligence * Inference * Optimization * Reinforcement learning * Transformer
