it establishes a temperature distribution model using K-means clustering and heat conduction equations, and optimizes heat dissipation parameters using the least squares method. Ultimately, it ...

It is worth noting that some researchers combine the APF algorithm with deep reinforcement learning algorithms to accelerate the training process. Hu et al. (2025) proposed a fuzzy A* quantum ...

Quantum machine learning takes classical data and encodes it in quantum states. The quantum computer can then uncover patterns in the data that would be hard for classical systems to detect.

In this paper, we introduce a predictive Q-learning deflection routing (PQDR) algorithm for buffer-less networks. Q-learning, one of the reinforcement learning (RL) algorithms, has been considered for ...

A hybrid intelligent algorithm integrating Q-learning is innovatively designed, using a genetic algorithm as the main framework while embedding a quay crane allocation module and dynamically selecting ...

Q-Learning Algorithm Bellman Equation in Use The Q-learning algorithm is implemented in the game to help the player "learn" the best way to move. The key concept behind Q-learning is the Bellman ...

The Q-Learning algorithm will be a formula used in this project and AI model. Names aside, Q-Learning refers to a formula used in a machine learning algorithm capable of “grade-school” level ...

In Q-learning, Q* represents the desired state in which an agent knows exactly the best action to take in every state to maximize its total expected reward over time. In math terms, it satisfies the ...

When beginning to study reinforcement learning, temporal difference learning is frequently used as an entry point. In order to elaborate on this concept and demonstrate the fundamentals of ...