Summary of Generation Of Geodesics with Actor-critic Reinforcement Learning to Predict Midpoints, by Kazumi Kasaura
Generation of Geodesics with Actor-Critic Reinforcement Learning to Predict Midpoints
by Kazumi Kasaura
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper introduces a novel approach for generating shortest paths on manifolds with infinitesimally defined metrics, achieving this by predicting midpoints recursively and leveraging an actor-critic method to learn midpoint prediction. The authors demonstrate the soundness of their approach and experimentally show that it outperforms existing methods in both local and global path planning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to find the shortest paths on curvy surfaces, called manifolds, when we don’t have a clear definition of how far apart points are. To do this, they use a special method that predicts middle points and makes an actor-critic model learn how to make those predictions. The results show that their approach is better than other methods for finding paths on these curvy surfaces. |
