Summary of Deep Gaussian Covariance Network with Trajectory Sampling For Data-efficient Policy Search, by Can Bogoclu and Robert Vosshall and Kevin Cremanns and Dirk Roos
Deep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy Search
by Can Bogoclu, Robert Vosshall, Kevin Cremanns, Dirk Roos
First submitted to arxiv on: 23 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 A novel approach to model-based reinforcement learning (MBRL) is proposed, leveraging probabilistic world models to enhance data efficiency by incorporating epistemic uncertainty in policy guidance and exploration. This uncertainty-aware learning yields more robust policies that are less sensitive to noisy observations compared to traditional methods. The combination of trajectory sampling and deep Gaussian covariance networks (DGCNs) is explored for solving MBRL problems in optimal control settings, outperforming other combinations of uncertainty propagation methods and probabilistic models on four well-known test environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to learn from experiences is being developed, which uses mathematical models of the world to make better decisions. By combining different ideas, researchers have created a method that can efficiently learn from data and adapt to changing situations. This approach is tested in various scenarios and shows improved performance over previous methods. |
Keywords
* Artificial intelligence * Reinforcement learning
