Summary of A Cantor-kantorovich Metric Between Markov Decision Processes with Application to Transfer Learning, by Adrien Banse et al.
A Cantor-Kantorovich Metric Between Markov Decision Processes with Application to Transfer Learning
by Adrien Banse, Venkatraman Renganathan, Raphaël M. Jungers
First submitted to arxiv on: 11 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 extends the Cantor-Kantorovich distance between Markov chains to the context of Markov Decision Processes (MDPs). The new metric is well-defined and efficiently computable given a finite horizon. This extension enables interesting applications in reinforcement learning, specifically forecasting the performance of transfer learning algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how to measure the similarity between different types of processes that help machines make decisions. By developing a new way to calculate this similarity, scientists can use it to predict how well certain algorithms will perform when applied to different situations. This could be very useful in areas like artificial intelligence and decision-making. |
Keywords
» Artificial intelligence » Reinforcement learning » Transfer learning
