Summary of When to Sense and Control? a Time-adaptive Approach For Continuous-time Rl, by Lenart Treven et al.
When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL
by Lenart Treven, Bhavya Sukhija, Yarden As, Florian Dörfler, Andreas Krause
First submitted to arxiv on: 3 Jun 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 paper presents a novel reinforcement learning (RL) framework called Time-adaptive Control & Sensing (TaCoS), which optimizes policies for continuous-time Markov decision processes. This framework is designed to reduce the number of interactions with the system, making it more efficient in applications where manual intervention is costly. The TaCoS formulation extends standard MDPs by predicting the duration of control actions, allowing state-of-the-art RL algorithms to retain performance while reducing interactions. The paper demonstrates significant improvements over discrete-time approaches and proposes OTaCoS, a model-based algorithm for TaCoS that achieves sublinear regret in systems with smooth dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computers learn from trying different things to control machines or devices. Right now, these computers are good at learning from small steps, but they’re not great when the steps take a long time or involve lots of effort. The researchers came up with a new way for these computers to learn that takes into account how much work each step requires. This new approach allows the computer to find the best solution while using fewer “tries” and making better decisions. |
Keywords
» Artificial intelligence » Reinforcement learning
