In this project we will learn to get the optimal playing policy in black jack using reinforcement learning.
The project will require developing a simulator of the environment and running different RL algorithms such as Q-learning, UCB and DQN.
Final results will be showing the ability to learn known results such as basic strategy and cards counting and in the latter stage results for some open questions in the field of optimal cards counting and optimal betting policy.