1_Cartpole_DQN_keras_model.py

1_Cartpole_DQN_keras_model.py

from keras.models import Model
from keras.layers import Input, Dense
from keras.optimizers import Adam, RMSprop

# Neural Network model for Deep Q Learning
def OurModel(input_shape, action_space):
    X_input = Input(input_shape)

    # 'Dense' is the basic form of a neural network layer
    # Input Layer of state size(4) and Hidden Layer with 512 nodes
    X = Dense(512, input_shape=input_shape, activation="relu", kernel_initializer='he_uniform')(X_input)

    # Hidden layer with 256 nodes
    X = Dense(256, activation="relu", kernel_initializer='he_uniform')(X)
    
    # Hidden layer with 64 nodes
    X = Dense(64, activation="relu", kernel_initializer='he_uniform')(X)

    # Output Layer with # of actions: 2 nodes (left, right)
    X = Dense(action_space, activation="linear", kernel_initializer='he_uniform')(X)

    model = Model(inputs = X_input, outputs = X, name='CartPole DQN model')
    model.compile(loss="mse", optimizer=RMSprop(lr=0.00025, rho=0.95, epsilon=0.01), metrics=["accuracy"])

    model.summary()
    return model