Improving the performance of Learned Controllers in Behavior Trees using Value Function Estimates at Switching Boundaries

TL;DR

This paper proposes a method to enhance behavior tree controllers by using value function estimates at switching boundaries, leading to potentially globally optimal overall control performance.

Contribution

It introduces a novel approach that leverages value function approximations to improve sub-controller coordination in behavior trees, achieving global optimality under certain conditions.

Findings

Using value function estimates improves overall controller performance.

The method can achieve global optimality when assumptions are met.

Applicable even with pre-existing sub-controllers under constraints.

Abstract

Behavior trees represent a modular way to create an overall controller from a set of sub-controllers solving different sub-problems. These sub-controllers can be created in different ways, such as classical model based control or reinforcement learning (RL). If each sub-controller satisfies the preconditions of the next sub-controller, the overall controller will achieve the overall goal. However, even if all sub-controllers are locally optimal in achieving the preconditions of the next, with respect to some performance metric such as completion time, the overall controller might be far from optimal with respect to the same performance metric. In this paper we show how the performance of the overall controller can be improved if we use approximations of value functions to inform the design of a sub-controller of the needs of the next one. We also show how, under certain assumptions, this leads to a globally optimal controller when the process is executed on all sub-controllers. Finally, this result also holds when some of the sub-controllers are already given, i.e., if we are constrained to use some existing sub-controllers the overall controller will be globally optimal given this constraint.