From Reinforcement Learning to Portfolio Management
Reinforcement learning(RL) is a highly applied branch of machine learning. It contains many fundamental algorithms like Q-learning and SARSA, which can be used to deal with some "tabularizable"(i.e. easy) Markov Chain Decision Process problems, for example, a car running through a maze. It also maintains the potential of absorbing neural networks structures (e.g. Deep Q-Network), to solve some rather complicated problems like portfolio management in a certain market. This project introduces tabular methods (Q-learning & SARSA), approximation method (Policy Gradient) and Network-embedded method (Deep Q-network) with some intuitive examples, and finally explores the potential application of RL in finance.