brain_dropout.py

# Building the Brain

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

class Brain(object):
    
    # BUILDING A FULLY CONNECTED NEURAL NETWORK DIRECTLY INSIDE THE INIT METHOD
    
    def __init__(self, learning_rate = 0.001, number_actions = 5):
        self.learning_rate = learning_rate
        
        # BUILDIND THE INPUT LAYER COMPOSED OF THE INPUT STATE
        states = Input(shape = (3,))
        
        # BUILDING THE FIRST FULLY CONNECTED HIDDEN LAYER WITH DROPOUT ACTIVATED
        x = Dense(units = 64, activation = 'sigmoid')(states)
        x = Dropout(rate = 0.1)(x)
        
        # BUILDING THE SECOND FULLY CONNECTED HIDDEN LAYER WITH DROPOUT ACTIVATED
        y = Dense(units = 32, activation = 'sigmoid')(x)
        y = Dropout(rate = 0.1)(y)
        
        # BUILDING THE OUTPUT LAYER, FULLY CONNECTED TO THE LAST HIDDEN LAYER
        q_values = Dense(units = number_actions, activation = 'softmax')(y)
        
        # ASSEMBLING THE FULL ARCHITECTURE INSIDE A MODEL OBJECT
        self.model = Model(inputs = states, outputs = q_values)
        
        # COMPILING THE MODEL WITH A MEAN-SQUARED ERROR LOSS AND A CHOSEN OPTIMIZER
        self.model.compile(loss = 'mse', optimizer = Adam(lr = learning_rate))