Multi agent reinforcement learning for UAV collision avoidance - Abdul Hamid, Nor Asilah Wati; Rezaee, Mohammad Reza; Ismail, Zurita (American Institute of Physics, 2024)
The proliferation of unmanned aerial vehicles (UAVs) across many sectors is seeing a fast growth trajectory, resulting in heightened congestion inside the airspace. As a result, the need to guarantee flight safety and mitigate the risk of accidents among unmanned aerial vehicles has emerged as a critical concern within the rapidly advancing realm of drone technology. Multi agent reinforcement learning presents a viable methodology for tackling these challenges, since it empowers drones to exhibit enhanced intelligence when operating in intricate surroundings alongside several agents. This article presents an examination of multi-agent reinforcement learning and its utilization in augmenting the safety of unmanned aerial vehicles. In this paper, we provide a pragmatic instantiation of multi-agent reinforcement learning, which encompasses the participation of several agents. The research results presented in this study provide evidence of the algorithm's efficacy in reducing drone collisions in intricate and highly populated settings, resulting in a significant rate of success.