Summary of Rich-observation Reinforcement Learning with Continuous Latent Dynamics, by Yuda Song et al.
Rich-Observation Reinforcement Learning with Continuous Latent Dynamics
by Yuda Song, Lili Wu, Dylan J. Foster, Akshay Krishnamurthy
First submitted to arxiv on: 29 May 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 The proposed RichCLD (Rich-Observation RL with Continuous Latent Dynamics) framework addresses the challenges of sample-efficiency and reliability in reinforcement learning algorithms for continuous settings with high-dimensional perceptual inputs. A new algorithm is introduced, which is provably statistically and computatorily efficient. The core of this algorithm is a novel representation learning objective that is amenable to practical implementation. This approach compares favorably to prior schemes in a standard evaluation protocol. Furthermore, the statistical complexity of the RichCLD framework is analyzed, revealing that certain notions of Lipschitzness are insufficient in the rich-observation setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning algorithms can learn from experience and make decisions based on their observations. However, these algorithms often struggle when they have to handle a lot of data or make decisions quickly. To address this issue, researchers introduced a new framework called RichCLD that helps agents learn more efficiently in situations where they have to deal with many different inputs. This framework is useful for applications like robotics, autonomous vehicles, and control systems. |
Keywords
» Artificial intelligence » Reinforcement learning » Representation learning
