Summary of Towards An Adaptable and Generalizable Optimization Engine in Decision and Control: a Meta Reinforcement Learning Approach, by Sungwook Yang et al.
Towards an Adaptable and Generalizable Optimization Engine in Decision and Control: A Meta Reinforcement Learning Approach
by Sungwook Yang, Chaoying Pei, Ran Dai, Chuangchuang Sun
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The proposed meta-reinforcement learning (RL) based optimizer enables fast adaptation to update model predictive control (MPC) controllers for solving different sequential decision-making problems in non-stationary environments, without requiring expert demonstrations. This optimizer learns to optimize the MPC controller updates by mimicking the expert performances, allowing it to adapt quickly to unseen control tasks with only a few shots of experience. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is being explored to improve model predictive control (MPC) for solving complex problems. Currently, experts need to demonstrate how to update the controllers, which can be time-consuming and expensive. This new method uses artificial intelligence (AI) to learn an optimizer that can adapt quickly to different situations without needing expert help. The results show that this AI-powered optimizer is effective in adapting to new situations with just a few examples. |
Keywords
* Artificial intelligence * Reinforcement learning
