Summary of Kernel-based Function Approximation For Average Reward Reinforcement Learning: An Optimist No-regret Algorithm, by Sattar Vakili and Julia Olkhovskaya
Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm
by Sattar Vakili, Julia Olkhovskaya
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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 paper proposes an optimistic algorithm for reinforcement learning (RL) that utilizes kernel ridge regression to predict the expected value function. This framework is highly versatile and has great representational capacity. In the infinite horizon average reward setting, also known as the undiscounted setting, the proposed algorithm establishes novel no-regret performance guarantees under kernel-based modeling assumptions. Additionally, a novel confidence interval for the kernel-based prediction of the expected value function is derived, applicable across various RL problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new approach to reinforcement learning that uses kernel ridge regression to predict the expected value function. This method has great representational capacity and can be used in a variety of situations. The algorithm is designed for the infinite horizon average reward setting and provides no-regret performance guarantees under certain assumptions. The paper also includes a confidence interval for predicting the expected value function, which can be applied to different RL problems. |
Keywords
» Artificial intelligence » Regression » Reinforcement learning
