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(PDF) Multi-Agent Reinforcement Learning Aided Intelligent UAV Swarm for Target Tracking
Source: researchgate.net
Article summary:
1. This paper proposes an end-to-end cooperative multi-agent reinforcement learning (MARL) scheme for target tracking with UAVs, which takes position information of UAV trackers and the target as input and outputs tracking strategies of UAV trackers.
2. The proposed MARL algorithm is theoretically proved to converge, and simulations show that it outperforms several popular DRL baselines in terms of convergence speed and ultimately achievable rewards.
3. To further increase the detection coverage, spatial information entropy is introduced in the tracking algorithm, and a propulsion power consumption model and energy saving strategy are also introduced to reduce power consumption and prolong the lifetime of the UAV tracking system.
Article analysis:
This article provides a comprehensive overview of a multi-agent reinforcement learning aided intelligent UAV swarm for target tracking. The authors present a novel framework from the perspective of comprehensive practical engineering considerations, including introducing a quantitative index (spatial information entropy) to boost information collection efficiency during tracking, introducing a realistic UAV flight energy consumption model to promote an energy-efficient TT scheme, and taking safe distance constraints into account in the reward function to avoid UAV-UAV and UAV-target collision. The proposed MARL algorithm is theoretically proved to converge, and simulations show that it outperforms several popular DRL baselines in terms of convergence speed and ultimately achievable rewards.
The article appears to be well researched with sufficient evidence provided for its claims. It presents both sides equally by providing an overview of related works on VT (vision based tracking) as well as RL (reinforcement learning) based tracking before presenting its own approach. Furthermore, potential risks are noted throughout the article such as transmission latency reduction due to light-weight position information sharing among UAVs, physical limitation of photographing equipments when using VT methods, etc., which makes it trustworthy and reliable overall.