Reimplemented osc managing with an OscManager static class
Created a basic subclass for OscAgent
Added the possibility of running an osc agent
Removed the call to init() inside the NeuralNetwork constructor.
Always increase episodes in episode() (even if terminated naturally)
To keep things clean, set memory to zero in NeuralNetwork constructor
Adjust episode in unit_test to 1000 + 1 (to account for the call to start())
Introduce two new DataSet:
- MultiDataSet that combines many DataSets into one
- MemoryDataSet that reads a DataSset and puts it into memory
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