Summary of Model Selection For Average Reward Rl with Application to Utility Maximization in Repeated Games, by Alireza Masoumian et al.
Model Selection for Average Reward RL with Application to Utility Maximization in Repeated Games
by Alireza Masoumian, James R. Wright
First submitted to arxiv on: 9 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT); 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 introduces , an online model selection algorithm for average reward reinforcement learning (RL) in Markov Decision Processes (MDPs). The algorithm aims to learn an optimal policy in an MDP with unknown structure, by selecting the most suitable model class from a set of M possibilities. The algorithm’s regret is shown to be O(M C_{m^}^2 {m^}(T,)), where C_{m^} represents the complexity of the simplest well-specified model class, and {m^}(T,) is its corresponding regret bound. The paper also demonstrates the algorithm’s application to a two-player simultaneous general-sum repeated game, where the learner aims to maximize its utility against an opponent with a fixed unknown limited memory strategy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new online model selection algorithm for average reward reinforcement learning (RL). The algorithm, called , helps a learner choose the best policy in a situation where they don’t know what kind of problem they’re facing. This is important because it can make decisions better and faster. The algorithm does this by trying to find the right “model” that explains how things work. It’s like trying to figure out what type of puzzle you’re dealing with, so you can solve it correctly. The paper shows that this algorithm works well in certain situations and is more efficient than other methods. |
Keywords
* Artificial intelligence * Reinforcement learning
