Summary of Q-learning For Quantile Mdps: a Decomposition, Performance, and Convergence Analysis, by Jia Lin Hau et al.
Q-learning for Quantile MDPs: A Decomposition, Performance, and Convergence Analysis
by Jia Lin Hau, Erick Delage, Esther Derman, Mohammad Ghavamzadeh, Marek Petrik
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a new Q-learning algorithm for quantile optimization in Markov decision processes (MDPs), which leverages a novel dynamic program (DP) decomposition. The proposed algorithm, designed for model-free reinforcement learning (RL), offers strong convergence and performance guarantees. In contrast to existing methods, this approach requires neither known transition probabilities nor solving complex saddle point equations. Numerical results demonstrate that the Q-learning algorithm converges to its DP variant and outperforms earlier algorithms in tabular domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for machines learning how to make decisions based on uncertainty. It’s like making choices when you’re not sure what will happen next. The method is called Q-learning, and it helps decide between different outcomes. This paper shows that this Q-learning works well in certain situations and can be used for other machine learning tasks too. |
Keywords
* Artificial intelligence * Machine learning * Optimization * Reinforcement learning
