q-learning algorithm

Network routing of drones for search and rescue operations is a critical challenge. This challenge arises due to the physical limitations of drones, adverse environmental conditions, and time constraints. In this paper, a novel approach for network routing of drones using the Q-Learning algorithm is proposed. This algorithm enables drones to automatically determine optimal paths in complex environments and adapt to environmental changes. Simulation results demonstrate that the Q-Learning algorithm can find shorter and more efficient routes compared to genetic algorithms. These findings highlight Q-Learning as a promising method for improving network routing of drones in search and rescue operations

This paper mainly aims to determine the optimal drug dosage for the purpose of reducing the population of cancer cells in melanoma patients. To do so, Reinforcement Learning method and the eligibility traces algorithm are employed, giving us the advantage of creating a compromise between the two algorithms of the reinforcement learning, being Monte-Carlo and Temporal Difference. Furthermore, it can be said that using this approach, there was no need to employ a mathematical model in the whole process. However, as its implementation on the real system was not possible, a delayed nonlinear mathematical model is used to investigate the performance of the proposed controller and simulate the behavior of the environment. It should be noted this mathematical model made use of no control method. This is the first time that population control of cancer cells is applied and tested on this model. To know of the optimal dosage of the drug, it should be mentioned that the drug is required to prevent the side effects on healthy/normal cells as much as possible. According to the obtained results, the eligibility traces algorithm is able to control and reduce the population of cancer cells through injecting the sub-optimal drug dose. This will increase the level of immunity in our body. Finally, to demonstrate the advantage of a selective method of increasing the rate of cancer cell death, this method is compared with the Q-learning algorithm and optimal control. By applying the fault to the sensor, the performance of the proposed controller to reduce cancer cells was investigated. The adaptability of the proposed method with the environment changes is checked afterwards. To this end, uncertainty in the system parameters and initial conditions are applied and the population of cancer cells are controlled in five melanoma patients. Moreover, having added noise to the system, it was shown that the eligibility traces algorithm is able to control the population of cancer cells and make it reach zero. Additionally, the convergence speed of both eligibility traces algorithm and Q learning algorithm in reducing the number of cancer cells for different learning rates was investigated.

Melanoma is one of the most dangerous types of cancers and every year, many people suffer from this cancer. Hence, quick diagnosis and treatment are significantly important for the physicians. In the recent decade, intelligent methods have attracted considerable attention for diagnosing and treating the melanoma. The main objective of this paper is determining the optimal dosage of the drug for the elimination of the cancer cells while preventing from the side effect of the drug on the normal cells. To this aim, the Q-learning algorithm is employed. In order to select the actions, a Case-Based Reasoning (CBR) policy is used, which is an accelerated heuristic policy. The considered policy has increased the learning speed and reduced the overall time, to reach the optimal policy. The half-life effect of the drug is also considered to obtain the side effect of the drug on the patientchr('39')s body, at each time step. In order to demonstrate Q-learning algorithm performance in cancer cells control and optimal dosage determination purposes, Q-learning is compared with two methods, including fix dosage injection method and Hamiltonian method, which is one of the most important optimal control methods. Finally, it is revealed that the total injected dosage by using Reinforcement Learning method (Q-learning) is significantly reduced within the whole period of time in comparison with employing the optimal control and a fixed dosage injection cases. The number of cancer cells is controlled, as well. It should be noted that by applying the noise and uncertainty to the system parameters and the initial conditions, the proposed method can successfully control the cancer cells.