  This thesis provides an overview of the recent advances in reinforcement
learning in pricing and hedging financial instruments, with a primary focus on
a detailed explanation of the Q-Learning Black Scholes approach, introduced by
Halperin (2017). This reinforcement learning approach bridges the traditional
Black and Scholes (1973) model with novel artificial intelligence algorithms,
enabling option pricing and hedging in a completely model-free and data-driven
way. This paper also explores the algorithm's performance under different state
variables and scenarios for a European put option. The results reveal that the
model is an accurate estimator under different levels of volatility and hedging
frequency. Moreover, this method exhibits robust performance across various
levels of option's moneyness. Lastly, the algorithm incorporates proportional
transaction costs, indicating diverse impacts on profit and loss, affected by
different statistical properties of the state variables.
