  In this letter we show how to improve the performance of backward chained
behavior trees (BTs) that use reinforcement learning (RL). BTs represent a
hierarchical and modular way of combining control policies into higher level
control policies. Backward chaining is a design principle for the construction
of BTs that combine reactivity with goal directed actions in a structured way.
The backward chained structure has also enabled convergence proofs for BTs,
identifying a set of local conditions that lead to the convergence of all
trajectories to a set of desired goal states. The key idea of this letter is to
improve performance of backward chained BTs by using the conditions identified
in a theoretical convergence proof to setup the RL problems for individual
controllers. In particular, previous analysis identified so-called active
constraint conditions (ACCs), that should not be broken in order to avoid
having to return to work on previously achieved subgoals. We propose a way to
setup the RL problems, such that they do not only achieve each immediate
subgoal, but also avoid violating the identified ACCs. The resulting
performance improvement depends on how often ACC violations occurred before the
change, and how much effort was needed to re-achieve them. The proposed
approach is illustrated in a dynamic simulation environment.
