… With the current rapid increase of logistics demands, traditional methods incur the … a novel
reinforcement learning algorithm called the Multi-Agent Attention Model that can solve routing …

… method that can predict the optimal number of vehicles in advance is urgently needed. In this
work, we propose a novel multi-agent reinforcement learning … constructs the route iteratively …

… This article presents a multi-agent … of Reinforcement Learning. For better introduction and
validation of the AMAM framework, this article uses the instantiation of the Vehicle Routing …

… parameters used during learning. We have given a fleet of cars in a multi-agent environment.
We … graph under traffic conditions, such that all routes are traversed not less than a defined …

… methods. … vehicle routing and deep multi-agent reinforcement learning. Section 3 provides
a formal introduction to coalitional games, coalitional bargaining and reinforcement learning. …

… method to handle DS-VRP. Our approach depends on multi-agent reinforcement learning
where we place our decision makers in nodes instead of vehicles and we use geometric …

… vehicles is highly related to solution exploration and is difficult to model. In this paper, we
propose a novel multi-agent reinforcement learning… Multi-vehicle routing problems with soft time …

… We propose to integrate energy scheduling and vehicle routing into one problem, which
significantly increases the problem complexity. To address this challenge, we propose a …

… To efficiently solve MRG, we formulate it as multi-agent reinforcement learning (MARL) and
… a multi-driver route choice task, we develop a multi-agent deep Q-learning approach in this …

… routing problems. This paper proposes a new multi-agent deep reinforcement learning (…
Extensive experiments are conducted to evaluate the performance of the proposed approach. …