Summary of Iterative Batch Reinforcement Learning Via Safe Diversified Model-based Policy Search, by Amna Najib et al.
Iterative Batch Reinforcement Learning via Safe Diversified Model-based Policy Search
by Amna Najib, Stefan Depeweg, Phillip Swazinna
First submitted to arxiv on: 14 Nov 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 In this paper, researchers propose a novel approach to offline reinforcement learning that enables continuous improvement of learned policies by iteratively collecting new data during deployment. The method, called iterative batch reinforcement learning, relies on previously collected interactions and ensemble-based model-based policy search. By incorporating safety and diversity criteria, the algorithm ensures efficient and informative data collection, ultimately leading to better decision-making in high-risk and cost-intensive applications like industrial control. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way of improving learned policies by collecting more data during deployment. It’s like refining a recipe based on how well it works in practice. The researchers use a special kind of learning called offline reinforcement learning, which doesn’t require direct interaction with the environment. Instead, they rely on pre-recorded interactions to learn what works best. This approach is useful for situations where trying new things might be expensive or dangerous. By continuously collecting and refining data, the learned policies can get better over time. |
Keywords
» Artificial intelligence » Reinforcement learning
