Summary of Operator World Models For Reinforcement Learning, by Pietro Novelli et al.
Operator World Models for Reinforcement Learning
by Pietro Novelli, Marco Pratticò, Massimiliano Pontil, Carlo Ciliberto
First submitted to arxiv on: 28 Jun 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 This paper presents a novel approach to Reinforcement Learning (RL) by combining Policy Mirror Descent (PMD) with learning a world model using conditional mean embeddings. The proposed method, POWR, leverages operator theory to derive a closed-form expression for the action-value function, enabling convergence rates to the global optimum. Preliminary experiments demonstrate the effectiveness of POWR in finite and infinite state settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for computers to learn and make decisions by combining two powerful tools: Policy Mirror Descent (PMD) and learning about the environment. The new method, called POWR, helps the computer find the best actions to take by using information about what happens next. This approach is important because it shows that RL can be more effective when combined with PMD. Early tests of this idea have been promising. |
Keywords
* Artificial intelligence * Reinforcement learning
