Learning of Behavior Trees for Autonomous Agents

TL;DR

This paper introduces a model-free approach using Genetic Programming to automatically learn Behavior Trees for autonomous agents, demonstrated through a Mario AI benchmark to improve game-playing in unknown environments.

Contribution

It presents a novel framework combining Genetic Programming with Behavior Trees to enable autonomous agents to learn effective behaviors without predefined models.

Findings

Successfully generated Behavior Trees for Mario AI to complete levels

Demonstrated adaptability to different difficulty levels and obstacles

Showed advantages over traditional transition system approaches

Abstract

Definition of an accurate system model for Automated Planner (AP) is often impractical, especially for real-world problems. Conversely, off-the-shelf planners fail to scale up and are domain dependent. These drawbacks are inherited from conventional transition systems such as Finite State Machines (FSMs) that describes the action-plan execution generated by the AP. On the other hand, Behavior Trees (BTs) represent a valid alternative to FSMs presenting many advantages in terms of modularity, reactiveness, scalability and domain-independence. In this paper, we propose a model-free AP framework using Genetic Programming (GP) to derive an optimal BT for an autonomous agent to achieve a given goal in unknown (but fully observable) environments. We illustrate the proposed framework using experiments conducted with an open source benchmark Mario AI for automated generation of BTs that can play the game character Mario to complete a certain level at various levels of difficulty to include enemies and obstacles.