On the Effect of the Layers' Number of Deep Neural Network for Improving the Reward of a Reinforcement Learning Robot

Neutrosophic and Information Fusion NIF 2836-7863 10.54216/NIF https://www.americaspg.com/journals/show/3057 2023 2023 On the Effect of the Layers' Number of Deep Neural Network for Improving the Reward of a Reinforcement Learning Robot Faculty of Informatics Engineering, Albaath University, Syria Talal Talal Faculty of Informatics Engineering, Albaath University, Syria Bahaa Mansoura The Q learning algorithm in reinforcement learning is one of the algorithms that allows the robot to learn the surrounding environment without the need for prior training samples with the principle of reward and punishment for the robot through interaction with the environment. Increasing the number of hidden layers of the deep neural network used and adjusting some of the higher parameters in it can increase the reward of the robot and thus obtain the best path to achieve the goal. 2024 2024 9 17 10.54216/NIF.030202 https://www.americaspg.com/articleinfo/39/show/3057