Table 1. Model training for DRL and DL.

References Algorithm/Methods for training machine learning model Pros and Cons of the algorithm
Gustavo Carneiro et al. [31] (2019)Alexendre Momeni et al. [34] (2019)Issa Ali et al. [36] (2018)C. Martinez et al. [37] (2018)Adriana Dinis et al. [40]. (2018)Zi Wang et al. [41] (2018)Leo Celi et al. [42] (2017)Ning Liu et al. [43] (2017) Deep Q Learning:-The deep Q-learning algorithm is a deep reinforcement learning based algorithm that combines deep learning based neural network architecture with reinforcement learning based Q-learning.-The algorithm was developed by google deep-mind in 2015 for testing ATARI gaming platform. It showed that deep Q-network based agent on given the pixels and game score in input form was able to outperform against all the already known algorithms and gave a similar accuracy of a human game tester when tested against a set of 49 games.-In deep Q-learning, we use a neural network to approximate the Q-value function. The state is given as the input and the Q-value of all possible actions is generated as the output. Pros:The Deep Q algorithm is a powerful algorithm that combines the feature extraction capability of CNN and reward enhancement capability of reinforcement learning to achieve a desired objective function.DQ learning agent enable an autonomous decision making with high accuracies and precision.Cons:DQN requires a large amount of time to train a reinforcement learning agent.The rewards and environment must be effectively defined.
Alexander Rakhlin et al. [44] (2018)Riku Turkki et al .[45] (2019)Dmitrii Bychkov et al. [46] (2018)Parampal S. Grewal et al. [47] (2018)Azam Hamidinekoo et al. [48] (2018) CNN (Convolutional Neural Network):-A CNN stands for convolutional neural network.-CNN is a combination of different layers. The four important ones are: input layer, convolutional layer, activation layer and pool layer. It is an important algorithm for image classification.1. Input Layer: This layer holds the raw input of image with width 32, height 32 and depth 3.2. Convolution Layer: This layer computes the output volume by computing dot product between all filters and image patch. Suppose we use total 12 filters for this layer we’ll get output volume of dimension 32 x 32 x 12.3. Activation Function Layer: This layer will apply element wise activation function to the output of convolution layer.4. Pool Layer: This layer is periodically inserted in the covnets and its main function is to reduce the size of volume which makes the computation fast reduces memory and also prevents from overfitting. Pros-CNN is a very powerful algorithm which is widely used for image classification and object detection. The hierarchical structure and powerful feature extraction capabilities from an image makes CNN a very robust algorithm for various image and object recognition tasks.Cons:-CNN requires a large amount of data to train the model.-The CNN output changes with the adversarial effects of input representations .i.e. the output changes as the input is changed.