Reinforcement Learning (RL) has led to several breakthroughs in AI. The use of the Q-learning (DQL) algorithm alone has helped people develop agents that play arcade games and board games at a superhuman level. More recently, RL, DQL, and similar methods have gained popularity in publications related to financial research. This book is among the first to explore the use of Reinforcement Learning methods in finance. One of the key algorithms in RL is deep Q-learning (DQL) that can be applied to a large number of dynamic decision problems. Popular examples are arcade games and board games, such as Go, in which RL and DQL algorithms have achieved superhuman performance in many instances. This has often happened despite the belief of experts that such feats would be impossible for decades to come. This book closes the gap in that it provides the required background in a concise fashion and otherwise focuses on the implementation of the algorithms in the form of self-contained Python code and the application to important financial problems. This book is intended as a concise, Python-based introduction to the major ideas and elements of RL and DQL as applied to finance. It should be useful to both students and academics as well as to practitioners in search of alternatives to existing financial theories and algorithms. The book expects basic knowledge of the Python programming language, object-oriented programming, and the major Python packages used in Data Science and Machine Learning, such as NumPy, Pandas, Matplotlib, Scikit-learn, and TensorFlow.

Reinforcement Learning for Finance: A Python-Based Introduction PDF

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