… An efficient fleet management strategy not only can … It is a challenging task to design an
effective fleet management … In this paper we propose to tackle the large-scale fleet management …

… In this article, we investigate the fleet management problem (multi-vehicle dispatching
problem) of ride-sharing platform with the objective of maximizing the order response rate. To …

… deep reinforcement learning algorithm for taxi dispatching. We first cast the fleet management
… Then, we develop a deep Q-network with action sampling policy (AS-DQN) to learn an …

… -agent mix hierarchical reinforcement learning method for fleet management. The problem
… (leader and follower) based on hierarchical reinforcement learning. The MIX module is …

… dynamic and demand aware fleet management framework for … Deep Reinforcement Learning
(RL), (5) Allowing for distributed inference at each vehicle while collectively optimizing fleet …

… In next subsection we discuss different simulators for fleet management and based on
them we discuss which reinforcement learning algorithm makes more sense to evaluate. …

… We propose an adaptive model-free deep … Deep reinforcement learning methodologies
are used for this adaptive modeling where transition probabilities are computed through Deep Q …

… It is a challenging task to design an effective fleet management strategy … fleet management
problem using reinforcement learning, and propose a contextual multi-agent reinforcement …

… fleet management by combining multi-agent reinforcement … as multi-agent reinforcement
learning, whose actionvalue … We use stochastic policy update rule over the graph with deep …

… we present a deep reinforcement learning approach for tackling the full fleet management and
… , where a central fleet management agent is responsible for decisionmaking for all drivers. …