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+<table><tr><td>Property group</td><td>Property Name</td><td>Description</td><td>Misc.</td><td>Note</td></tr><tr><td>Model File</td><td>Save Model File</td><td>Decide whether to save model file.</td><td>Required</td><td>Yes, No</td></tr><tr><td>Model File</td><td>Model File Path</td><td>Set the path for the model file to be saved.</td><td>Conditionally Required</td><td/></tr><tr><td>Selection options</td><td>Model Setting</td><td>Select the gms file to load in model.</td><td>Required</td><td/></tr><tr><td>Selection options</td><td>Discount Rate(gamma)</td><td>Set the discount rate(gamma) for the future reward.</td><td>Required</td><td>0&lt;real number &lt;1</td></tr><tr><td>Selection options</td><td>Actor Learning Rate</td><td>Specify the learning rate for the Actor network.</td><td>Required</td><td>0&lt;real number &lt;1</td></tr><tr><td>Selection options</td><td>Critic Learning Rate</td><td>Specify the learning rate of the Critic neural network.</td><td>Required</td><td>0&lt;real number &lt;1</td></tr><tr><td>Selection options</td><td>Epsilon</td><td>Specify the probability of choosing actions. When the probability is set to 0, the action with the highest Q-value is selected; when set to 1, actions are chosen completely at random.</td><td>Required</td><td>0&lt;real number &lt;1</td></tr><tr><td>Selection options</td><td>Epsilon Decay Rate</td><td>Specify the decay rate of epsilon.</td><td>Required</td><td>0&lt;real number &lt;1</td></tr><tr><td>Selection options</td><td>Minimum Epsilon Value</td><td>Specify the minimum value of Epsilon.</td><td>Required</td><td>0&lt;real number &lt;1</td></tr><tr><td>Selection options</td><td>Min Replay Memory Size</td><td>Specifies the minimum size of replay memory for starting training.</td><td>Required</td><td>0&lt;Integer&lt;N</td></tr><tr><td>Selection options</td><td>Max Replay Memory Size</td><td>Specify the maximum number of samples to be stored in replay memory.</td><td>Required</td><td>Integer</td></tr><tr><td>Selection options</td><td>Batch Size</td><td>Specify the number of sample batches to extract from memory for training.</td><td>Required</td><td>Integer</td></tr><tr><td>Selection options</td><td>Max Episodes</td><td>Specify the maximum number of episodes to learn.</td><td>Required</td><td>Integer</td></tr><tr><td>Selection options</td><td>Max Steps</td><td>Specify the number of maximum steps in an episode.</td><td>Required</td><td>Integer</td></tr><tr><td>Selection options</td><td>Target Tolerance</td><td>Specify the allowable error margin for the target value when defining the environmental reward.</td><td>Required</td><td>Integer</td></tr><tr><td>Selection options</td><td>Batch Normalization</td><td>Decide whether to use batch normalization.</td><td>Required</td><td>Yes, No</td></tr><tr><td>Selection options</td><td>Set control, target Variable</td><td>Specify control and target variables</td><td>Required, Button</td><td/></tr><tr><td>Selection options</td><td>Output Layer Activation Function</td><td>Specifies the Activation Function of the output layer.</td><td/><td>Linear, Sigmoid, ReLU, Leaky ReLU, SoftMax</td></tr><tr><td>Hidden Layer Configuration</td><td>Add hidden layer</td><td>Add hidden layer.</td><td>Optional</td><td/></tr><tr><td>Hidden Layer Configuration</td><td>Remove Last Hidden Layer</td><td>Remove the last hidden layer</td><td>Optional</td><td/></tr><tr><td>Hidden Layer #</td><td>Number of nodes</td><td>Specify the number of nodes for each specified hidden layer</td><td>Required</td><td>Integer</td></tr><tr><td>Hidden Layer #</td><td>Activation Function</td><td>Select the activation function for the specified hidden layer.</td><td>Required</td><td>Linear, Sigmoid, ReLU, Leaky ReLU, SoftMax</td></tr></table>