… -agent collision avoidance algorithm based on a novel application of deep reinforcement
learning, (ii… [8] VR Desaraju and JP How, “Decentralized path planning for multi-agent teams in …

… collision avoidance policy for efficient distributed multi-agent navigation. Our method formulates
an agent’s navigation strategy as a deep … of frames of collision avoidance data collected …

… is learned through deep reinforcement learning (… a deep multi-agent reinforcement learning
(DMARL) problem. We benchmark the developed PCAM systems according to the collision …

… of multi-ship cooperative collision avoidance. In this research, multi-agent deep reinforcement
learning (MADRL) was used to address the problem of intelligent collision avoidance and …

… collision avoidance policy learned from the proposed method is able to find time efficient,
collision-… (ORCA) framework [1] has been popular in crowd simulation and multi-agent systems. …

… for the collision-avoidance task in multi-agent systems. … to the insight of the widely used deep
model ResNet (Xie et al., … with the collision avoidance in easy cases, so that deep-learned …

… collision avoidance decision-making algorithm by a data-driven method and adopts the
Multi-agent Deep Reinforcement … This paper proposes a multi-agent collision avoidance …

… Abstract—We present a novel algorithm (DeepMNavigate) for global multi-agent navigation
in dense scenarios using deep reinforcement learning (DRL). Our approach uses local and …

… advantages of the multi-agent method will become more obvious. Thence this paper defines
… -ship collision avoidance as a multi-agent problem, and uses the multi-agent reinforcement …

… achieve obstacle avoidance and collision avoidance in the process of multi-agent formation
… the expected goal of multi-agent formation obstacle avoidance and has stronger portability …