Removed the need to have a TupleDataSet in BatchRLTrainer by adding static helper functions in TupleDataSet
Implement properly the Q-fitted neural net algorithm by reinitialising the net at every episode
Allocate the example in DataSet::init() now to avoid code repetition
Bug fix in setExample(): the memcpy was not copying all data
Applied the new DataSet::init() allocation to DiskXFileDataSet as well
Major fix in NeuralNetwork backpropagate: the output layer error was not set properly in non-linear output mode
Shuffling tools
Introduce a SubDataSet class
Many changes:
- Offers an optional softmax policy
- Can set the random seed
- Sigmoidal (nonlinear) NeuralNet output
Added an offset agent id to multirun.py
Added my own formats to ignores
Batch RL script
Linear output of neural network is false by default
Redesign test suite in response to latest fix in NeuralNetwork