Summary of Online Reinforcement Learning with Passive Memory, by Anay Pattanaik and Lav R. Varshney
Online Reinforcement Learning with Passive Memory
by Anay Pattanaik, Lav R. Varshney
First submitted to arxiv on: 18 Oct 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 This paper introduces an online reinforcement learning algorithm that utilizes pre-collected data from the environment, known as “passive memory,” to improve performance during online interactions. By leveraging this passive memory, the algorithm achieves near-minimax optimal regret guarantees, outperforming previous approaches. The quality of this passive memory is found to directly impact the sub-optimality of incurred regret, making it a crucial component for successful decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about an artificial intelligence (AI) system that gets better at making decisions by using information it learned before. Imagine playing a game where you can use clues from past games to make smart moves. This AI algorithm does just that, and it’s really good! It uses something called “passive memory” to help it decide what to do next. The results show that this system is near perfect and makes very few mistakes. |
Keywords
* Artificial intelligence * Reinforcement learning
