pyqlearning.functionapproximator package¶
Submodules¶
pyqlearning.functionapproximator.cnn_fa module¶

class
pyqlearning.functionapproximator.cnn_fa.
CNNFA
(batch_size, layerable_cnn_list, cnn_output_graph, learning_rate=1e05, computable_loss=None, opt_params=None, verificatable_result=None, pre_learned_path_list=None, pre_learned_output_path=None, cnn=None, verbose_mode=False)[source]¶ Bases:
pyqlearning.function_approximator.FunctionApproximator
Convolutional Neural Networks(CNNs) as a Function Approximator.
CNNs are hierarchical models whose convolutional layers alternate with subsampling layers, reminiscent of simple and complex cells in the primary visual cortex.
This class demonstrates that a CNNs can solve generalisation problems to learn successful control policies from observed data points in complex Reinforcement Learning environments. The network is trained with a variant of the Qlearning algorithm, with stochastic gradient descent to update the weights.
The Deconvolution also called transposed convolutions “work by swapping the forward and backward passes of a convolution.” (Dumoulin, V., & Visin, F. 2016, p20.)
References
 Dumoulin, V., & V,kisin, F. (2016). A guide to convolution arithmetic for deep learning. arXiv preprint arXiv:1603.07285.
 Masci, J., Meier, U., Cireşan, D., & Schmidhuber, J. (2011, June). Stacked convolutional autoencoders for hierarchical feature extraction. In International Conference on Artificial Neural Networks (pp. 5259). Springer, Berlin, Heidelberg.
 Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013). Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602.

get_model
()[source]¶ object of model as a function approximator, which has cnn whose type is pydbm.cnn.pydbm.cnn.convolutional_neural_network.ConvolutionalNeuralNetwork.

inference_q
(next_action_arr)[source]¶ Infernce QValue.
Parameters: next_action_arr – np.ndarray of action. Returns: np.ndarray of QValues.

learn_q
(predicted_q_arr, real_q_arr)[source]¶ Infernce QValue.
Parameters:  predicted_q_arr – np.ndarray of predicted QValues.
 real_q_arr – np.ndarray of real QValues.

loss_list
¶ getter

model
¶ object of model as a function approximator, which has cnn whose type is pydbm.cnn.pydbm.cnn.convolutional_neural_network.ConvolutionalNeuralNetwork.
pyqlearning.functionapproximator.lstm_fa module¶

class
pyqlearning.functionapproximator.lstm_fa.
LSTMFA
(batch_size, lstm_model, seq_len=10, learning_rate=1e05, computable_loss=None, opt_params=None, verificatable_result=None, verbose_mode=False)[source]¶ Bases:
pyqlearning.function_approximator.FunctionApproximator
LSTM Networks as a Function Approximator.
Originally, Long ShortTerm Memory(LSTM) networks as a special RNN structure has proven stable and powerful for modeling longrange dependencies.
The Key point of structural expansion is its memory cell which essentially acts as an accumulator of the state information. Every time observed data points are given as new information and input to LSTM’s input gate, its information will be accumulated to the cell if the input gate is activated. The past state of cell could be forgotten in this process if LSTM’s forget gate is on. Whether the latest cell output will be propagated to the final state is further controlled by the output gate.
References
 Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoderdecoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
 Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., & Shroff, G. (2016). LSTMbased encoderdecoder for multisensor anomaly detection. arXiv preprint arXiv:1607.00148.
 Zaremba, W., Sutskever, I., & Vinyals, O. (2014). Recurrent neural network regularization. arXiv preprint arXiv:1409.2329.

get_model
()[source]¶ object of model as a function approximator, which has lstm_model whose type is pydbm.rnn.lstm_model.LSTMModel.

inference_q
(next_action_arr)[source]¶ Infernce QValue.
Parameters: next_action_arr – np.ndarray of action. Returns: np.ndarray of QValues.

learn_q
(predicted_q_arr, real_q_arr)[source]¶ Infernce QValue.
Parameters:  predicted_q_arr – np.ndarray of predicted QValues.
 real_q_arr – np.ndarray of real QValues.

loss_list
¶ getter

model
¶ object of model as a function approximator, which has lstm_model whose type is pydbm.rnn.lstm_model.LSTMModel.