Deep Reinforcement Learning-Based Distributed Formation Control for Multi-Agents

This study proposes a distributed formation control framework for multi-agent systems by integrating deep reinforcement learning (DRL) with interdisciplinary methodologies from control theory, robotics, and artificial intelligence. Aimed at addressing collaborative formation challenges in dynamic environments, the approach uses graph-theoretic modeling for formation-keeping and obstacle avoidance and dynamic Attention-based Advantage Actor-Critic (DA-A2C) algorithm to solve collaborative formation problems in dynamic environments like autonomous drone swarms and robotic fleets. To balance formation consistency and obstacle repulsion, this strategy optimizes neighbor perception dynamic weight allocation using the dynamic Ω-zone mechanism. Experimental validations in simulated environments with static/moving obstacles show superior performance over SA2C, MATD3, and DDPG baselines, covering formation control and obstacle avoidance in complex situations and outperforming other algorithms in adaptability and robustness. This research can improve cross-domain applications in intelligent transportation, industrial automation, and emergency response systems. The method works in multi-robot formation systems, and future study will focus on 3D environment generalization and efficient communication protocols.