Energy-Aware Multi-Agent Proximal Policy Optimization with Depletion Safety Constraints for Multi-Robot Coordination

Multi-robot systems operating on battery power face fundamental constraints through which energy limitations directly impact mission success. The existing multi-agent reinforcement learning approaches optimize for task performance without explicit energy consideration, leading to inefficient consumption and depletion risk. This paper presents a framework for energy-aware multi-agent coordination that treats battery management as a safety constraint, rather than an optimization objective. We introduce Energy-Aware Multi-Agent Proximal Policy Optimization (EA-MAPPO) with energy-augmented observations and shaped rewards and extend it to Safe Energy-Aware MAPPO (SEA-MAPPO) combining predictive action masking with safety-oriented reward shaping. An experimental validation on the Georgia Tech Robotarium with 7 agents demonstrates that SEA-MAPPO reaches 95% goal completion 19× faster than standard MAPPO, requiring only 0.5 M environment steps versus 9.4 M. Throughout training, SEA-MAPPO reduces cumulative depletion events by 93% compared to MAPPO while maintaining superior energy efficiency. SEA-MAPPO achieves 100% goal completion versus 81.5% for MAPPO at the same training budget. Physical deployment on GTernal robots without fine-tuning achieves 100% goal completion with zero depletion events across 70 robot-trials, with the energy predictor achieving R2=0.89 with measured power consumption.