Summary of Sublinear Regret For a Class Of Continuous-time Linear-quadratic Reinforcement Learning Problems, by Yilie Huang et al.
Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems
by Yilie Huang, Yanwei Jia, Xun Yu Zhou
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY); Optimization and Control (math.OC)
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 A reinforcement learning (RL) algorithm is developed for continuous-time linear-quadratic (LQ) control problems with diffusions. The proposed model-free approach learns the optimal policy parameter directly without requiring knowledge of model parameters or their estimations. The algorithm includes an exploration schedule and a regret analysis, achieving a regret bound of O(N^3/4) up to a logarithmic factor after N learning episodes. Simulation results validate the theoretical findings, demonstrating the effectiveness and reliability of the proposed method. Compared to recent model-based stochastic LQ RL studies adapted to state- and control-dependent volatility settings, the proposed algorithm shows better performance in terms of regret bounds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is used to solve a special type of problem called linear-quadratic control problems. These problems involve making decisions based on information about the current situation and trying to achieve a specific goal. The new method doesn’t need to know the exact rules governing the situation, it can learn by trial and error. This approach is tested in computer simulations and is found to work well. It’s even better than other methods that are specifically designed for this type of problem. |
Keywords
* Artificial intelligence * Reinforcement learning
