  Behavior Trees are commonly used to model agents for robotics and games,
where constrained behaviors must be designed by human experts in order to
guarantee that these agents will execute a specific chain of actions given a
specific set of perceptions. In such application areas, learning is a desirable
feature to provide agents with the ability to adapt and improve interactions
with humans and environment, but often discarded due to its unreliability. In
this paper, we propose a framework that uses Reinforcement Learning nodes as
part of Behavior Trees to address the problem of adding learning capabilities
in constrained agents. We show how this framework relates to Options in
Hierarchical Reinforcement Learning, ensuring convergence of nested learning
nodes, and we empirically show that the learning nodes do not affect the
execution of other nodes in the tree.
