Summary of Constrained Reinforcement Learning with Average Reward Objective: Model-based and Model-free Algorithms, by Vaneet Aggarwal et al.
Constrained Reinforcement Learning with Average Reward Objective: Model-Based and Model-Free Algorithms
by Vaneet Aggarwal, Washim Uddin Mondal, Qinbo Bai
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 RL models excel in sequential decision-making, leveraging diverse domains such as robotics, autonomous driving, recommendation systems, supply chain optimization, biology, mechanics, and finance. The primary goal is to maximize average rewards while adhering to real-world constraints. This paper explores [insert relevant technical details from the abstract]. By applying novel methods and benchmarks, researchers aim to improve RL’s adaptability and scalability for practical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RL helps make smart choices in many areas like robots, self-driving cars, recommendation systems, logistics, biology, mechanics, and finance. The main goal is to get a good average score while following real-world rules during the learning process. This paper is about [insert high-level summary of the abstract]. |
Keywords
* Artificial intelligence * Optimization
