Summary of Projection by Convolution: Optimal Sample Complexity For Reinforcement Learning in Continuous-space Mdps, By Davide Maran et al.
Projection by Convolution: Optimal Sample Complexity for Reinforcement Learning in Continuous-Space MDPs
by Davide Maran, Alberto Maria Metelli, Matteo Papini, Marcello Restelli
First submitted to arxiv on: 10 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents a solution for learning an -optimal policy in continuous-space Markov decision processes (MDPs) with smooth Bellman operators. By utilizing a generative model, the authors achieve rate-optimal sample complexity through a perturbed version of least-squares value iteration with orthogonal trigonometric polynomials as features. The key to this solution is a novel projection technique based on harmonic analysis ideas. The proposed method achieves a sample complexity of (^{-2-d/(+1)}), which recovers the state-of-the-art result for Lipschitz MDPs and generalizes the rate for low-rank MDPs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds a way to learn an optimal policy in complex situations where actions have many possibilities. It uses a special type of math called harmonic analysis to solve this problem quickly and accurately. This method can work well even when there are many possible states and actions, which is important for real-world problems like self-driving cars or robots. |
Keywords
» Artificial intelligence » Generative model
