Improving the Performance of Backward Chained Behavior Trees that use Reinforcement Learning

TL;DR

This paper enhances backward chained behavior trees that utilize reinforcement learning by incorporating theoretical convergence conditions to prevent ACC violations, leading to improved performance in goal-directed control tasks.

Contribution

It introduces a method to configure RL problems within BTs using convergence proof conditions, specifically ACCs, to avoid subgoal violations and improve overall efficiency.

Findings

Performance improved by avoiding ACC violations

Method effective in dynamic simulation environments

Reduces rework on previously achieved subgoals

Abstract

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.