| Description
| This course provides a comprehensive introduction to Reinforcement Learning (RL) and its applications in finance. Topics include Markov Decision Processes (MDP), Dynamic Programming, Monte Carlo methods, Temporal-Difference learning, Policy Gradient approaches, and Deep Reinforcement Learning. Through lectures and coding exercises, students will learn how to formulate financial problems within the RL framework—defining states, actions, and reward functions—and how to train agents to make sequential investment decisions.
The course integrates theory with practical applications such as portfolio optimization, asset allocation, hedging strategies, and algorithmic trading. Students will gain hands-on experience implementing RL algorithms in Python and applying them to real-world financial datasets. Challenges unique to financial markets, including stochasticity, risk constraints, non-stationarity, and evaluation difficulties, will also be discussed.
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