Summary of Reinforcement Learning For Control Of Non-markovian Cellular Population Dynamics, by Josiah C. Kratz and Jacob Adamczyk
Reinforcement Learning for Control of Non-Markovian Cellular Population Dynamics
by Josiah C. Kratz, Jacob Adamczyk
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Populations and Evolution (q-bio.PE)
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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 A novel machine learning approach, combining reinforcement learning (RL) and deep learning, is developed to optimize drug dosing strategies for controlling cell populations exhibiting phenotypic plasticity. This phenomenon, observed in various organisms and cell types, enables cells to adapt to changing environments and leverage past experiences to survive stressors. The authors focus on dynamical models that switch between resistant and susceptible states, seeking exact solutions when underlying system parameters are unknown or complex memory-based systems are involved. By applying model-free deep RL, the proposed approach recovers exact solutions and effectively controls cell populations even in the presence of long-range temporal dynamics, measurement noise, and dynamic memory strength. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cells can adapt to changing environments and remember past experiences to survive stressors. Scientists have developed a new way to control cells using machine learning. They used a type of artificial intelligence called reinforcement learning (RL) to find the best strategy for giving medicine to cells that can change how they work in response to their environment. This approach worked even when there was noise in the data and the cells’ memory changed over time. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Reinforcement learning
