Summary of Learning Infinite-horizon Average-reward Linear Mixture Mdps Of Bounded Span, by Woojin Chae et al.
Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span
by Woojin Chae, Kihyuk Hong, Yufan Zhang, Ambuj Tewari, Dabeen Lee
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 A novel algorithm for learning infinite-horizon average-reward linear mixture Markov decision processes (MDPs) is proposed in this paper, which achieves a nearly minimax optimal regret upper bound of (d) over T time steps. The algorithm uses the technique of running value iteration on a discounted-reward MDP approximation with clipping by the span, and combines this with a weighted ridge regression-based parameter estimation scheme. This approach is shown to converge and bound the associated variance term due to random transitions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to solve a type of problem called Markov decision processes (MDPs) has been developed. MDPs are used to make decisions when there’s uncertainty about the outcome. The new algorithm does this by using something called value iteration, which helps it figure out the best decision. It also uses some tricks to make sure it doesn’t get stuck or make too many mistakes. This is important because it helps us understand how to solve MDPs in a way that’s both efficient and accurate. |
Keywords
» Artificial intelligence » Regression
