Multi-agent Cooperative Encirclement Method Based on Reinforcement Learning

This paper introduces a reinforcement learning-based multi-agent cooperative encirclement method to tackle target tracking and encirclement in dynamic stochastic environments. The Soft Actor-Critic (SAC) algorithm is first employed as the backbone algorithm for multi-agent policy learning, wherein the spatial consistency, task completion, and cooperative constraints—alongside rewards for obstacle avoidance, formation angles, pursuit, and stability are taken into account in a conjunctive manner, which not only allievate the sparse reward problem but also strengthens the robustness against perception uncertainty. Simulations results show that agents are capable of forming a plausible encircling trajectory in 10 seconds (±0.1m distance fluctuation, <10° angle deviation), and recovering stability in 12 seconds. A semi physical test is carried out to validate the robustness of this method, demonstrating the effectiveness in terms of environmental adaptability and rea-time operation.