ACM Computing Surveys (CSUR), ISSN: 0360-0300, vol. 52, issue 1, article no. 6
In machine learning, Reinforcement Learning (RL) is an important tool
for creating intelligent agents that learn solely through experience.
One particular subarea within the RL domain that has received great
attention is how to define macro-actions, which are temporal
abstractions composed of a sequence of primitive actions. This subarea,
loosely called skill acquisition, has been under development for several
years and has led to better results in a diversity of RL problems.
Among the many skill acquisition approaches, graph-based methods have
received considerable attention. This survey presents an overview of
graph-based skill acquisition methods for RL. We cover a diversity of
these approaches and discuss how they evolved throughout the years.
Finally, we also discuss the current challenges and open issues in the
area of graph-based skill acquisition for RL.