Drone Swarm Energy Management

TL;DR

This paper introduces an analytical framework combining POMDP and DDPG reinforcement learning for adaptive energy management and cooperative control in drone swarms operating under uncertainty, enhancing mission success and efficiency.

Contribution

It extends DDPG with belief-state representation for robust decision-making in partially observable environments, enabling scalable cognitive swarm autonomy.

Findings

Significant improvement in mission success rates

Enhanced energy efficiency over baseline methods

Robust decision-making in uncertain environments

Abstract

This note presents an analytical framework for decision-making in drone swarm systems operating under uncertainty, based on the integration of Partially Observable Markov Decision Processes (POMDP) with Deep Deterministic Policy Gradient (DDPG) reinforcement learning. The proposed approach enables adaptive control and cooperative behavior of unmanned aerial vehicles (UAVs) within a cognitive AI platform, where each agent learns optimal energy management and navigation policies from dynamic environmental states. We extend the standard DDPG architecture with a belief-state representation derived from Bayesian filtering, allowing for robust decision-making in partially observable environments. In this paper, for the Gaussian case, we numerically compare the performance of policies derived from DDPG to optimal policies for discretized versions of the original continuous problem. Simulation results demonstrate that the POMDP-DDPG-based swarm control model significantly improves mission success rates and energy efficiency compared to baseline methods. The developed framework supports distributed learning and decision coordination across multiple agents, providing a foundation for scalable cognitive swarm autonomy. The outcomes of this research contribute to the advancement of energy-aware control algorithms for intelligent multi-agent systems and can be applied in security, environmental monitoring, and infrastructure inspection scenarios.