The aim of this workshop is to discuss current ideas from computer science, psychology and neuroscience regarding learning and control of hierarchically structured behavior. Psychological research has long emphasized that human behavior is hierarchically structured. Indeed, a hierarchical organization of human behavior that matches the hierarchical structure of real-world problems has been the focus of much empirical and theoretical research, and has played a pivotal role in research on organized, goal-directed behavior. Behavioral hierarchy has been of longstanding interest within neuroscience as well, where it has been considered to relate closely to prefrontal cortical function. The prefrontal cortex, which, with its high cognitive functions, remains the most poorly understood area of the brain, has been repeatedly implicated in supporting and executing hierarchical learning and control. In yet a third field, recent developments within machine learning have led to the emergence of 'hierarchical reinforcement learning'. This line of research has begun investigating in depth how optimal control can learn, and make use of, hierarchical structures, specifically, how hierarchies of skills (also termed options, macros or temporally abstract actions) could by learned and utilized optimally. The workshop will bring together front-line researchers from each of these fields, with the aim of gleaning new insights from the integration of knowledge from these somewhat disparate areas of active research.