This project fails to find optimal strategy for Hanabi using a neural network and a Deep-Q-Learning approach.

The notebooks in this repo highlight some problems in this implementation.

• LearningRate shows how high learning rate prevents convergence
• Inizialization shows how too big initialization values (i.e. greater than 1e-3 in absolute value) prevent convergence
• QUnlimited shows how, without a compensation inside the code, for high gamma (i.e. > 0.5) Q grows exponentially
• QFeedback shows how introducing a cutting for very high value predicted Q values saturate
• despite the name GradientDeath shows how this algorithm is unable to learn. In a previous version the output layer had dimension 1 and, if the last hidden layer was small and with relu activation, gradient saturated very fast

The logic of the implementation is in the Game*.py files.

• Game.py is the last version, used in all the notebooks except QUnlimited
• GameUnlimited.py doesn't cut the returned values of Q function and is used for QUnlimited
• GameDummy.py has lower dimensionality