Summary of Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity, by Philip Amortila et al.
Reinforcement Learning under Latent Dynamics: Toward Statistical and Algorithmic Modularity
by Philip Amortila, Dylan J. Foster, Nan Jiang, Akshay Krishnamurthy, Zakaria Mhammedi
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 investigates the application of reinforcement learning (RL) in complex environments where agents interact with high-dimensional observations. The authors highlight that RL is often used to model systems with simple underlying dynamics, but this assumption is not always valid in real-world scenarios. To address this knowledge gap, the researchers aim to understand the fundamental statistical requirements and algorithmic principles for RL under latent dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning helps machines make decisions by interacting with their environment. Usually, we assume that the things happening behind the scenes are simple, but what if they’re actually really complex? This paper looks at how well current methods work when dealing with these more complicated scenarios. The authors want to figure out why some approaches struggle and find new ways to improve reinforcement learning. |
Keywords
* Artificial intelligence * Reinforcement learning
