Summary of Information-theoretic Minimax Regret Bounds For Reinforcement Learning Based on Duality, by Raghav Bongole et al.
Information-Theoretic Minimax Regret Bounds for Reinforcement Learning based on Duality
by Raghav Bongole, Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering, Mikael Skoglund
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT)
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 The proposed research focuses on developing robust policies for agents acting in unknown environments. The goal is to achieve high cumulative rewards across all possible environments by minimizing the maximum regret over different environment parameters. To this end, the study explores minimax regret in Markov Decision Processes (MDPs) with a finite time horizon. Building on concepts from supervised learning, such as minimum excess risk and minimax excess risk, the researchers leverage recent bounds on Bayesian regret to derive information-theoretic bounds for minimax regret. The contributions include defining a suitable minimax regret framework, establishing minimax theorems, and applying these theorems in various scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about helping machines make good decisions even when they don’t know what’s going on. It wants to find ways that agents can find a “good” policy that works well no matter what happens. To do this, it looks at how much regret an agent would feel if it chose the wrong path. The researchers use ideas from learning and decision-making to figure out how to get the best possible outcome. They come up with new rules and ways to analyze the data to help agents make better decisions. |
Keywords
» Artificial intelligence » Supervised
