GAPPO-ERW: an imitation reinforcement learning approach for AGV path planning

GAPPO-ERW: an imitation reinforcement learning approach for AGV path planning

연구 분야: Artificial Intelligence

학회: The Journal of Supercomputing

In the realm of Automated Guided Vehicle (AGV) path planning, this paper introduces an innovative Generative Adversarial Proximal Policy Optimization (GAPPO) approach, which integrates Generative Adversarial Imitation Learning (GAIL) with Proximal Policy Optimization (PPO). Additionally, an Expert-driven Adaptive Reward Weighting (ERW) strategy is incorporated to enhance the decision-making capabilities of the agent in complex environments. Through simulation validation in a virtual environment, the feasibility of this method in optimizing AGV systems has been demonstrated. Comparative results with other reinforcement learning techniques reveal that both GAPPO and GAPPO-ERW surpass traditional reinforcement learning methods in terms of path planning effectiveness and model training efficiency, showcasing their significant potential in enhancing AGV operational efficiency and flexibility.