Summary of A Robust Quantile Huber Loss with Interpretable Parameter Adjustment in Distributional Reinforcement Learning, by Parvin Malekzadeh et al.
A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement Learning
by Parvin Malekzadeh, Konstantinos N. Plataniotis, Zissis Poulos, Zeyu Wang
First submitted to arxiv on: 4 Jan 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 This paper introduces a novel approach to Distributional Reinforcement Learning (RL) by proposing a generalized quantile Huber loss function based on Wasserstein distance calculation between Gaussian distributions. The classical quantile Huber loss function often relies on heuristic threshold parameter selection, which can lead to suboptimal performance and poor generalization. The new loss function captures noise in predicted and target quantile values, enhancing robustness against outliers. This innovation is tested on Atari games and a recent hedging strategy using distributional RL, demonstrating its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for robots to learn how to make good decisions. Right now, they use a special math problem to figure out what’s best, but this can be tricky and might not work well all the time. The scientists in this study created a new way to solve this problem that is more reliable and works better. They tested it on video games and a real-world task, and it performed well. |
Keywords
* Artificial intelligence * Generalization * Loss function * Reinforcement learning
