3 accepted papers
Coverage path planning is a critical problem in multi-UAV systems for daily inspection and patrol missions, requiring coordinated movements of UAVs along predetermined trajectories to cover the entire map. Existing methods are often inadequate in considering both workload balance and motion efficien
Motivated by the emerging use of multi-agent reinforcement learning (MARL) in engineering applications such as networked robotics, swarming drones, and sensor networks, we investigate the policy evaluation problem in a fully decentralized setting, using temporal-difference (TD) learning with linear…
The present paper develops a novel aggregated gradient approach for distributed machine learning that adaptively compresses the gradient communication. The key idea is to first quantize the computed gradients, and then skip less informative quantized gradient communications by reusing outdated gradi…