Deep Hierarchical Reinforcement Learning

The biological paradigm of learning by trial and error has motivated tremendous success in the field of Machine Learning. While Reinforcement Learning (RL) rests behind a myriad of breakthroughs, its practical application to real-world scenarios remains an open question. This thesis addresses the three challenges of restricted scalability, reduced robustness and limited practical viability through the lens of hierarchies serving as abstractions of composite behavior. Novel evolutionary RL methods present an evolving hierarchy which provisions scalability among members of its population. Novel energy based RL schemes, on the other hand, minimize surprise utilizing low energy configurations among members of the multi agent hierarchy. The framework of energy-based surprise minimization steers practical application of hierarchical RL to the setting of trade execution. The end result of this study is a hierarchical scheme demonstrating trade patterns analogous to humans with this thesis serving as a motivation for application of RL to practical problems.M.A.S