Summary of Scalable Online Exploration Via Coverability, by Philip Amortila et al.
Scalable Online Exploration via Coverability
by Philip Amortila, Dylan J. Foster, Akshay Krishnamurthy
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); 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 The paper proposes exploration objectives in reinforcement learning, particularly for high-dimensional domains requiring function approximation. The authors introduce the concept of policy optimization objectives that enable downstream maximization of any reward function, serving as a framework to study exploration systematically. Within this framework, they present a novel objective, L_1-Coverage, which generalizes previous schemes and satisfies three fundamental desiderata: encouraging exploration in unexplored regions, promoting diversity in the learned policy, and supporting downstream optimization. The authors demonstrate the effectiveness of their approach on various benchmark tasks and highlight its potential applications in robotics, healthcare, and finance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a robot that needs to explore new places or a doctor trying to find the best treatment for a patient. In these situations, it’s crucial to discover new things and try different approaches. The paper proposes a way to encourage this exploration using special objectives in computer programming. They introduce a new method called L_1-Coverage, which helps robots and doctors learn from their experiences and make better decisions. This approach has the potential to revolutionize various fields, including robotics, healthcare, and finance. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
