Summary of Policy Learning For Balancing Short-term and Long-term Rewards, by Peng Wu et al.
Policy Learning for Balancing Short-Term and Long-Term Rewards
by Peng Wu, Ziyu Shen, Feng Xie, Zhongyao Wang, Chunchen Liu, Yan Zeng
First submitted to arxiv on: 6 May 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 The paper formalizes a new framework for learning optimal policies that balance long-term and short-term rewards. It presents identifiability results for both rewards under mild assumptions, and derives semiparametric efficiency bounds along with consistency and asymptotic normality of their estimators. The approach reveals that short-term outcomes can improve the estimator of the long-term reward. A principled policy learning method is developed, and convergence rates of regret and estimation errors are derived. Experimental results demonstrate the practical applicability of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make good decisions by balancing what happens in the short-term with what happens in the long-term. Right now, we often focus too much on one or the other. The researchers created a new way to find the best balance between these two goals. They showed that knowing about short-term outcomes can actually help us better predict what will happen in the long-term. This new approach can be used to make decisions and is shown to work well in practice. |
