'''Create a recurrent neural network to compute a control policy.
'''
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.recurrent import SimpleRNN
def create_rnn():
"""Create a recurrent neural network to compute a control policy.
Reference:
Koutnik, Jan, Jurgen Schmidhuber, and Faustino Gomez. "Evolving deep
unsupervised convolutional networks for vision-based reinforcement
learning." Proceedings of the 2014 conference on Genetic and
evolutionary computation. ACM, 2014.
"""
model = Sequential()
model.add(SimpleRNN(output_dim=3, stateful=True, batch_input_shape=(1, 1, 3)))
model.add(Dense(input_dim=3, output_dim=3))
model.compile(loss='mse', optimizer='rmsprop')
return model
def calculate_rnn_output(model, input, multiple=False):
"""Calculates the output of the RNN. Use vector=False to indicate that a
single input is being passed.
"""
output = model.predict(input)
if multiple:
output = output.reshape(output.shape[0], output.shape[1])
else:
output = output.reshape(output.shape[1])
return output
