Python: Advanced Guide to Artificial Intelligence

Q-Learning with Q-Network  or Deep Q Network (DQN) 

In the DQN, we replace the Q-Table with a neural network (Q-Network) that will learn to respond with the optimal action as we train it continuously with the explored states and their Q-Values. Thus, for training the network we need a place to store the game memory:

1. Implement the game memory using a deque of size 1000:

memory = deque(maxlen=1000)

2. Next, build a simple hidden layer neural network model, q_nn:

from keras.models import Sequentialfrom keras.layers import Densemodel = Sequential()model.add(Dense(8,input_dim=4, activation='relu'))model.add(Dense(2, activation='linear'))model.compile(loss='mse',optimizer='adam')model.summary()q_nn = model

The Q-Network looks like this:

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