Reward-Centering Deep Reinforcement Learning for Multiagent Coordinated Interception in Orbital Pursuit-Evasion Game

Maneuverable threat poses significant risks to the operational safety of spacecraft in orbit, thereby jeopardizing mission success in the contested space environment. To address the coordinated orbital game between multiple pursuers (spacecraft) and one evader (threat), this paper proposes a reward-centering multi-agent deep deterministic policy gradient (MADDPG) algorithm, which has two-folds: Firstly, a relative dynamical model consideringJ2perturbation is established to characterize orbital interactions among multiple players, laying a physics-based foundation for decision-making. Secondly, an improved multi-agent reinforcement learning framework is constructed with two components. For individual spacecraft of multiple pursuers, a state-driven exploration-exploitation tradeoff mechanism is incorporated to avoid suboptimal strategy convergence. For the spacecraft group (namely multiple pursuers), reward functions are designed to align individual behaviors with collective objectives. Furthermore, a reward-centering mechanism along with theoretical analysis is introduced to dynamically calibrate the reward baseline, which stabilizes policy update via reducing gradient variance. Simulation results show the proposed algorithm enables spacecraft to autonomously develop coordinated strategies, ultimately confining the threat within predefined interception threshold.