Summary of Variance-reduced Policy Gradient Approaches For Infinite Horizon Average Reward Markov Decision Processes, by Swetha Ganesh et al.
Variance-Reduced Policy Gradient Approaches for Infinite Horizon Average Reward Markov Decision Processes
by Swetha Ganesh, Washim Uddin Mondal, Vaneet Aggarwal
First submitted to arxiv on: 2 Apr 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 Medium Difficulty Summary: This research paper presents two innovative Policy Gradient-based methods for solving infinite horizon average reward Markov Decision Processes. The first approach utilizes Implicit Gradient Transport to reduce variance, achieving an expected regret of O(T^3/5). The second method leverages Hessian-based techniques, resulting in an expected regret of O(sqrt(T)). These advancements significantly surpass the current state-of-the-art, which achieves a regret of O(T^3/4). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: Researchers have developed new ways to solve complex decision-making problems. They created two methods that can be used for long-term planning in situations where rewards are averaged out over time. One method uses a clever trick to reduce uncertainty, allowing it to make better decisions than before. The other method is based on mathematical concepts and also makes significant improvements. These advancements have the potential to positively impact various fields, such as finance or healthcare. |
