Summary of Linear Bellman Completeness Suffices For Efficient Online Reinforcement Learning with Few Actions, by Noah Golowich and Ankur Moitra
Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions
by Noah Golowich, Ankur Moitra
First submitted to arxiv on: 17 Jun 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 research paper presents a novel approach to reinforcement learning (RL) with function approximation using value iteration, which generates approximations to the optimal value function by solving regression problems. The study focuses on learning an optimal policy in the online model of RL with linear function approximation under Bellman completeness assumptions. While statistically efficient algorithms exist, they rely on global optimism principles requiring nonconvex optimization problem solutions. This paper provides the first polynomial-time algorithm for RL under linear Bellman completeness when the number of actions is constant. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to learn optimal policies in situations where value iteration is used with function approximation. It looks at how well this approach works online, using linear functions to approximate the best policy. The researchers found that existing algorithms for this problem are efficient but require solving hard optimization problems. They created an algorithm that solves these problems efficiently and can be used in real-world scenarios. |
Keywords
* Artificial intelligence * Optimization * Regression * Reinforcement learning
