@Test public void when_callingCreateValueLabels_expect_INDArrayWithCorrectShape() { INDArray result = sut.createLabels(4); assertArrayEquals(new long[] { 1, 3, 4 }, result.shape()); }
@Override public INDArray createLabels(int trainingBatchSize) { return Nd4j.create(1, actionSpaceSize, trainingBatchSize); }
RecurrentNStepQLearningHelper extends NStepQLearningHelper { @Override public INDArray createLabels(int trainingBatchSize) { return Nd4j.create(1, actionSpaceSize, trainingBatchSize); } }
RecurrentNStepQLearningHelper extends NStepQLearningHelper { @Override public INDArray createLabels(int trainingBatchSize) { return Nd4j.create(1, actionSpaceSize, trainingBatchSize); } RecurrentNStepQLearningHelper(int actionSpaceSize); }
RecurrentNStepQLearningHelper extends NStepQLearningHelper { @Override public INDArray createLabels(int trainingBatchSize) { return Nd4j.create(1, actionSpaceSize, trainingBatchSize); } RecurrentNStepQLearningHelper(int actionSpaceSize); @Override INDArray createLabels(int trainingBatchSize); @Override INDArray getExpectedQValues(INDArray allExpectedQValues, int idx); @Override void setLabels(INDArray labels, long idx, INDArray data); @Override INDArray getTargetExpectedQValuesOfLast(IOutputNeuralNet target, List<StateActionPair<Integer>> trainingBatch, INDArray features); }
RecurrentNStepQLearningHelper extends NStepQLearningHelper { @Override public INDArray createLabels(int trainingBatchSize) { return Nd4j.create(1, actionSpaceSize, trainingBatchSize); } RecurrentNStepQLearningHelper(int actionSpaceSize); @Override INDArray createLabels(int trainingBatchSize); @Override INDArray getExpectedQValues(INDArray allExpectedQValues, int idx); @Override void setLabels(INDArray labels, long idx, INDArray data); @Override INDArray getTargetExpectedQValuesOfLast(IOutputNeuralNet target, List<StateActionPair<Integer>> trainingBatch, INDArray features);  }