Summary of Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure For Decision-making, by Vivek Myers et al.
Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making
by Vivek Myers, Chongyi Zheng, Anca Dragan, Sergey Levine, Benjamin Eysenbach
First submitted to arxiv on: 24 Jun 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 A novel approach to planning, control, and reinforcement learning is proposed in this paper. The authors develop a temporal distance metric that satisfies the triangle inequality, which enables efficient estimation of shortest paths even in stochastic settings. This breakthrough is achieved by leveraging contrastive learning and quasimetrics. The proposed method outperforms prior approaches in both controlled experiments and benchmark suites. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to plan and make decisions when reaching goals. It’s about creating a special kind of distance that helps computers find the shortest path to get things done. The authors use a technique called contrastive learning to create this distance, which is very useful because it works well even when things are uncertain or changing. They test their approach in different situations and show that it can help machines learn more quickly than before. |
Keywords
* Artificial intelligence * Reinforcement learning
