Mastering Blackjack: Combining Reinforcement Learning with Deep Reinforcement Learning

Mastering Blackjack is a get-into Reinforcement Learning project.
Reinforcement Learning is a branch of machine learning where an agent preforms in an environment and gets rewarded for his action. The agent goal is to learn a behavior policy that will maximize his reward over time.
In our project we studied the game of Blackjack, introduced RL algorithms such as Monte-Carlo Learning, Q – Learning and DQN, tested their performance with respect to the game and compared & analyzed their results.
The project goals included: Getting into the field of Reinforcement Learning and Deep Reinforcement Learning, Learn advanced coding in PyTorch, Get familiar with & implement algorithms from academic papers and get familiar with working on open-source projects and simulators.