Summary of Ocmdp: Observation-constrained Markov Decision Process, by Taiyi Wang et al.
OCMDP: Observation-Constrained Markov Decision Process
by Taiyi Wang, Jianheng Liu, Bryan Lee, Zhihao Wu, Yu Wu
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Systems and Control (eess.SY)
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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 Observation-Constrained Markov Decision Process (OCMDP) tackles decision-making challenges in cost-sensitive environments where observations are expensive. This framework learns both observation and control strategies simultaneously, balancing the costs of acquiring information with its benefits. A model-free deep reinforcement learning algorithm is developed to manage complexity, separating sensing and control components of the policy. Experimental results on a simulated diagnostic task and a realistic healthcare environment using HeartPole demonstrate substantial reduction in observation costs and outperformance of baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists developed a new way to make decisions when it’s expensive to gather information. They created a system that learns how to decide what information to gather and how to use that information to make good choices. This helps reduce the cost of gathering information while still making smart decisions. The approach was tested on two different scenarios and showed significant improvements over previous methods. |
Keywords
* Artificial intelligence * Reinforcement learning
