Summary of Adaptive Event-triggered Reinforcement Learning Control For Complex Nonlinear Systems, by Umer Siddique et al.
Adaptive Event-triggered Reinforcement Learning Control for Complex Nonlinear Systems
by Umer Siddique, Abhinav Sinha, Yongcan Cao
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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 a novel reinforcement learning control method for complex nonlinear systems with uncertainties, capable of jointly learning control and communication policies while reducing computational overhead. By augmenting the state space with accrued rewards, the algorithm determines optimal triggering conditions without explicit learning, leading to an adaptive non-stationary policy. The approach is demonstrated through several numerical examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to control complex systems that change over time. It combines two types of policies – one for controlling the system and another for deciding when to send information about the system’s state. This combination helps reduce the number of things the algorithm needs to learn, making it more efficient. The approach is tested on several examples and shown to be effective. |
Keywords
* Artificial intelligence * Reinforcement learning
