Learning Behavior Trees for Automated Guided Vehicles via Genetic and Reinforcement Methods

Behavior Trees (BTs) is a robust framework for decision-making that is highly applicable in automated guided vehicles (AGVs), offering a way to manage complex tasks and respond to dynamic changes in their environment. To enhance the adaptability and efficiency of BTs in AGVs, we introduce a hybrid approach that integrates Genetic Programming (GP) and Reinforcement Learning (RL). The GP evolves the BT structure by selecting potential actions from an action pool, guided by tailored constraints that ensure the trees remain interpretable and relevant to AGV tasks. Meanwhile, to mitigate node dependency issues in BTs, we employ RL, which incorporates a parameter-dependent dynamic updating (PDDU) algorithm to monitor and manage the relationships between parameters. Furthermore, we implement a weighted ϵ-greedy algorithm to refine the parameter update process. Our methodology is validated through simulated AGV scenarios, demonstrating that the evolved BT significantly improve AGV autonomy. This innovative fusion of GP and RL techniques sets a foundation for future developments in AGV technology, with potential applications extending beyond the factory floor to any environment where AGVs are deployed.