Summary of Optimistic Q-learning For Average Reward and Episodic Reinforcement Learning, by Priyank Agrawal and Shipra Agrawal
Optimistic Q-learning for average reward and episodic reinforcement learning
by Priyank Agrawal, Shipra Agrawal
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents an optimistic Q-learning algorithm for minimizing regret in average reward reinforcement learning under certain assumptions about the underlying Markov Decision Process (MDP). The setting generalizes the episodic setting and is less restrictive than previous literature on model-free algorithms. The algorithm achieves a regret bound of (H^5 S), where S and A are state and action spaces, and T is the horizon. The key innovation is introducing an operator, which is shown to have strict contraction in span under the given assumption. The algorithm design combines ideas from episodic Q-learning to estimate and apply this operator iteratively. This work provides a unified view of regret minimization in both episodic and non-episodic settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn better in situations where we don’t know exactly what will happen next. It’s like playing a game without knowing the rules, but trying to do well anyway. The researchers came up with a new way to play that does really well compared to other ways people have tried before. This is important because it can help us make decisions when things are uncertain. |
Keywords
* Artificial intelligence * Reinforcement learning
