Fast Multi-Class Vehicle Cooperative Path Optimization in Complex Urban V2X Transportation: A Novel Parallel Multi-Agent Reinforcement Learning Approach

Urban road traffic systems are advancing into sophisticated networks, underscoring the importance of real-time collaborative decision-making. This study tackles the intricate challenge of cooperative path planning under complex urban conditions, taking into account a variety of vehicle types and their respective priorities. While conventional path planning techniques struggle with such intricate coordination, reinforcement learning, though theoretically capable, is hindered by its limited model reusability and protracted training times. To address these issues, we present a novel parallel multi-agent reinforcement learning strategy for path planning that is adaptable to various vehicle types. The problem is initially cast as a multi-agent Markov Decision Process (MDP), followed by the introduction of a parallel training approach within the Q-learning framework. This approach leverages tensor computation to transform the Q-table, state, and reward, thereby markedly accelerating the training process. Empirical simulations demonstrate the approach's efficacy, achieving a 0.84% reduction in training time (from approximately 771.611 seconds to 0.654 seconds), achieving a 93.94% lower probability of path overlap though the total distance increased by 7.69%.