Summary of Geometric Active Exploration in Markov Decision Processes: the Benefit Of Abstraction, by Riccardo De Santi et al.
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction
by Riccardo De Santi, Federico Arangath Joseph, Noah Liniger, Mirco Mutti, Andreas Krause
First submitted to arxiv on: 18 Jul 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 explores how to use Reinforcement Learning (RL) algorithms to design experiments for complex systems. Specifically, it focuses on Active Exploration (AE), a framework that relaxes the optimization problem into Convex RL. However, AE is currently not scalable due to the vastness of experiment spaces typical in scientific discovery applications. To address this limitation, the authors bridge AE and MDP homomorphisms, which enable agents to leverage known geometric structures for statistical and computational efficiency improvements. The paper extends the formalism of MDP homomorphisms to Convex RL and presents an analysis that formally captures the benefit of abstraction via homomorphisms on sample efficiency. It also proposes the Geometric Active Exploration (GAE) algorithm, which is theoretically and experimentally analyzed in environments motivated by scientific discovery problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps scientists design better experiments for complex systems using Reinforcement Learning (RL). The authors create a new way to use RL to explore large spaces of possible experiments. This approach is called Active Exploration (AE), but it’s not very efficient because the spaces are too big. To fix this, they connect AE with another method called MDP homomorphisms, which helps agents understand how the experiment space is structured. By doing this, scientists can make better decisions about what experiments to run and when. The authors also create a new algorithm called Geometric Active Exploration (GAE) that can be used in real-world applications. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
