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QNetwork.py
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QNetwork.py
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from keras import layers, models, optimizers
from keras.utils import plot_model
from keras import backend as K
class QNetwork:
"""QNetwork Model."""
def __init__(self, state_size, action_size):
"""Initialize parameters and build model.
Params
======
state_size (int): Dimension of each state
action_size (int): Dimension of each action
"""
self.state_size = state_size
self.action_size = action_size
self.build_model()
def build_model(self):
"""Build a Q network that maps (state, action) pairs -> Q-values."""
# Define input layers
states = layers.Input(shape=(self.state_size,), name='states')
actions = layers.Input(shape=(self.action_size,), name='actions')
# Add hidden layer(s) for state pathway
net_states = layers.Dense(units=128, activation='relu')(states)
net_states = layers.Dense(units=128, activation='relu')(net_states)
# Add hidden layer(s) for action pathway
net_actions = layers.Dense(units=128, activation='relu')(actions)
net_actions = layers.Dense(units=128, activation='relu')(net_actions)
# Combine state and action pathways
net = layers.Add()([net_states, net_actions])
net = layers.Activation('relu')(net)
# Add final output layer to prduce action values (Q values)
Q_values = layers.Dense(units=1, name='q_values')(net)
# Create Keras model
self.model = models.Model(inputs=[states, actions], outputs=Q_values)
#plot_model(QNetwork, to_file='QNetwork.png', show_shapes=True)
# Define optimizer and compile model for training with built-in loss function
self.model.compile(optimizer='adam', loss='mse')