## 3 ML Techniques for Adaptive Plasma System
### Reinforcement Learning  
In machine learning (ML), algorithms are used to train and construct models using large datasets containing relevant information, such as input features (e.g., varying operational parameters of the adaptive plasma system) and target labels (e.g., treatment responses by the cancer cells). The objective is to analyze the datasets, identify patterns and relationships between input features and target labels, and adjust the internal parameters of the algorithm iteratively to improve its predictive accuracy (e.g., optimizing parameters in the adaptive plasma system for selective cytotoxicity towards cancer cells). The goal is to minimize the difference between the model's predictions and the actual outputs during training, making the model \"trained\" and capable of making accurate predictions. Different ML approaches, such as reinforcement learning (RL) and deep learning (DL), have different methods to update their parameters during training. For example, in the RL process, the agent (the adaptive plasma system) learns, becomes trained, and improves its behavior in an environment (cancer cells) through its interaction experience [123]. It takes action, receives feedback in the form of rewards and penalties based on the effectiveness of its treatment strategies, and uses this feedback to update its internal parameters and improve its decision making. Rewards may be associated with positive outcomes, such as a successful reduction in cancer cell viability, while penalties could be linked to less desirable effects. The system uses this feedback to update its internal parameters and improve its decision-making processes, aiming to find an optimal strategy that maximizes cumulative rewards over time. [PERSON] et al. [119] developed an adaptive plasma framework for CAP cancer treatments using the integration of empirical dynamic modeling and reinforcement learning (Figure 3). The empirical dynamic model was constructed using data collected from in vitro experi  
Figure 2: A scatter chart comparing the increase in the application of machine learning in cancer and CAP, based on search results in PubMed and Google Scholar using two search strings, (“Machine Learning”) AND (Cancer Treatment) AND (“Cold Atmospheric Plasma”). ments [18] that provided information on how cancer cell viability changes over time under various CAP treatment conditions (e.g., treatment duration from 0 to 180 \(s\) and the discharge voltage between 3.16 kV and 3.71 kV). The empirical data obtained from in vitro experiments may represent specific scenarios and treatment conditions. To estimate for a wide range of treatment scenarios, a Gaussian process, a type of statistical modeling tool that helps the model capture the variability and uncertainty inherent in real-world clinical settings, was incorporated. The CAP cancer treatment was then modeled as a Markov decision process (MDP) and conjugated with safe Q-learning. The MDP represents the sequence of states and actions in the treatment process. Each state corresponds to a specific situation or condition of the cancer cells (e.g., cancer cell viability), and each action represents a treatment strategy that the adaptive plasma system can take (e.g., treatment duration, discharge voltage). MDP helps in defining the states, actions, and transition probabilities in the treatment process, but it does not inherently provide the optimal policy for selecting actions that lead to the best long-term rewards. Q-learning, a specific algorithm designed for reinforcement learning tasks [124], helps the adaptive plasma system learn from its interactions with the environment and update its decision-making process by estimating the expected cumulative rewards for different actions in different states. This enables the system to make better decisions and optimize the treatment plan to achieve safer and more personalized cancer treatment. The study uses in vitro data to model cancer cell responses, potentially limiting their real-world applicability, but Gaussian processes aid in generalization.