CartPole_q_rnn.py 文件源码

def naiveQN(layer,activation = 'tanh'):
    model = Sequential()
    model.add(SimpleRNN(layer[1], input_shape = (None, layer[0]),return_sequences=True))
    for i in range(2,len(layer) - 1):
        model.add(Dense(layer[i], input_dim = layer[i-1]))
        model.add(Activation(activation))
    model.add(Dense(layer[-1], input_dim = layer[-2]))
    optimizer = RMSprop(lr = ETA, decay = 0.01)
    model.compile(optimizer= optimizer, loss='mean_squared_error')

    return model