A Cantor-Kantorovich Metric Between Markov Decision Processes with Application to Transfer Learning
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Abstract (arXiv)
We extend the notion of Cantor-Kantorovich distance between Markov chains introduced by (Banse et al., 2023) in the context of Markov Decision Processes (MDPs). The proposed metric is well-defined and can be efficiently approximated given a finite horizon. Then, we provide numerical evidences that the latter metric can lead to interesting applications in the field of reinforcement learning. In particular, we show that it could be used for forecasting the performance of transfer learning algorithms.
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References · 8
- 2020Transfer Learning in Deep Reinforcement Learning: A Surveycited by 259
- 2024Reinforcement learningcited by 236
- 2020Quantifying Differences in Reward Functionscited by 62
- 2020REPAINT: Knowledge Transfer in Deep Reinforcement Learningcited by 14
- 2018On Computing the Total Variation Distance of Hidden Markov Modelscited by 8
- 2018Target Transfer Q-Learning and Its Convergence Analysiscited by 6
- 2014On the Total Variation Distance of Labelled Markov Chainscited by 4
- 2023Data-driven abstractions via adaptive refinements and a Kantorovich metric [extended version]cited by 3
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