A Policy-Guided Reinforcement Learning Method for Encirclement Control in Multiobstacle Environment

The problem of multiagent encirclement with multiobstacle collision avoidance (EMOCA) has been challenging since it is difficult to balance the tradeoff between surrounding a mobile target and avoiding obstacles simultaneously. To address the EMOCA problem, we proposed a novel policy-guided reinforcement learning (RL) method, namely, multiregulator-assisted RL for encirclement control (MRA-RLEC) which leverages the jump-start learning and curriculum learning (CL) mechanism to enhance training efficiency. MRA-RLEC divides the complex encirclement task into a sequence of subtasks, progressively increasing in difficulty. In this process, multiple regulators are utilized to adjust various training aspects, including encirclement condition, obstacle avoidance, and the transition from guide to learned policy execution. Besides, a global encirclement reward decomposition (GERD) method is presented to alleviate reward sparsity, and we design a bidirectional communication protocol to reduce communication. Extensive experiments are carried out to showcase the robustness and superiority of our method, and the practical applicability of MRA-RLEC is demonstrated through experiments conducted in the robot operating system 2 (ROS2)-based simulation platform, Gazebo, using a self-designed omnidirectional vehicle model.