Research and Application of Multi-Robot Collaboration Strategies Based on Deep Reinforcement Learning

With the development of science and technology, multi-robot systems have shown great potential in the fields of intelligent manufacturing and disaster relief due to their efficient collaboration and flexible adaptability. However, realizing efficient collaboration in dynamic and uncertain environments still faces challenges. This study focuses on the application of deep reinforcement learning in multi-robot collaboration, aiming to improve the efficiency and robustness of system collaboration by constructing models, designing collaboration strategies, and implementing simulation experiments. Deep reinforcement learning utilizes the powerful representation capability of deep neural networks to process complex states and action spaces, and facilitates the robots to continuously optimize their collaboration strategies through trial and error. The study proposes a deep Q-network (DQN)-based multi-robot collaboration strategy and optimizes the joint action selection by combining the attention mechanism to improve the decision-making efficiency. The strategy significantly improves the average ball control time and action execution success rate of the multi-robot system as verified by simulation experiments in soccer robot matches, demonstrating the effectiveness of deep reinforcement learning in complex multi-robot collaboration tasks. The study not only enriches the application theory of deep reinforcement learning, but also provides strong support for the intelligence and autonomy of multi-robot systems.