Naive implementation of a Q-learning agent, which given the states, possible actions and the resulting rewards tries to learn the optimal policy (state => action mappings) for the process.
Q-learning on Wikipedia: https://en.wikipedia.org/wiki/Q-learning
The learning is done by the blackjack.Player class, supported by a
simulator, which orchestrates the training and testing phases, and a
dealer, which enforces the rules and orchestrates individual rounds of
the game.