Deep-Q-Based Reinforcement Learning Method to Predict Accuracy of Atari Gaming Set Classification

A pivotal acquisition model designed to formulate control strategies directly from high-level perceptual data using reinforcement learning is introduced by us. The training of this model, a convolutional neural network, was done using a different Q-learning algorithm. An output is produced in the form of a value function that indicates anticipated future earnings after processing raw image inputs. Recent demonstrations of cutting-edge performance in reinforcement learning tasks have been made by these neural networks, which leverage Deep Q-Networks (DQNs) based on the Q-Learning algorithm. The planning process for DQNs is significantly different from that of standard deep neural networks, resulting in significant challenges in specific reinforcement learning scenarios.