scholarly journals SAMoD: Shared Autonomous Mobility-on-Demand using Decentralized Reinforcement Learning

2018 ◽  
Author(s):  
Maxime Gueriau ◽  
Ivana Dusparic
Keyword(s):  
Reinforcement Learning ◽  
On Demand ◽  
Autonomous Mobility

scholarly journals Deep Reinforcement Learning based Charging Pricing for Autonomous Mobility-on-Demand System

2021 ◽  
pp. 1-1
Author(s):  
Ying Lu ◽  
Yanchang Liang ◽  
Zhaohao Ding ◽  
Qiuwei Wu ◽  
Tao Ding ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Demand System ◽  
On Demand ◽  
Autonomous Mobility

scholarly journals Rebalancing Autonomous Vehicles using Deep Reinforcement Learning

2022 ◽  
Vol 16 ◽  
pp. 646-652
Author(s):  
Jiajie Dai ◽  
Qianyu Zhu ◽  
Nan Jiang ◽  
Wuyang Wang
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Supply And Demand ◽  
Efficient Operation ◽  
Demand And Supply ◽  
On Demand ◽  
Demand Information ◽  
Autonomous Mobility ◽  
Demand Patterns

The shared autonomous mobility-on-demand (AMoD) system is a promising business model in the coming future which provides a more efficient and affordable urban travel mode. However, to maintain the efficient operation of AMoD and address the demand and supply mismatching, a good rebalancing strategy is required. This paper proposes a reinforcement learning-based rebalancing strategy to minimize passengers’ waiting in a shared AMoD system. The state is defined as the nearby supply and demand information of a vehicle. The action is defined as moving to a nearby area with eight different directions or staying idle. A 4.6 4.4 km2 region in Cambridge, Massachusetts, is used as the case study. We trained and tested the rebalancing strategy in two different demand patterns: random and first-mile. Results show the proposed method can reduce passenger’s waiting time by 7% for random demand patterns and 10% for first-mile demand patterns.

scholarly journals On-Demand Channel Bonding in Heterogeneous WLANs: A Multi-Agent Deep Reinforcement Learning Approach

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2789 ◽  
Author(s):  
Hang Qi ◽  
Hao Huang ◽  
Zhiqun Hu ◽  
Xiangming Wen ◽  
Zhaoming Lu
Keyword(s):  
Reinforcement Learning ◽  
Transmission Rate ◽  
Single Agent ◽  
Time Of Day ◽  
Action Space ◽  
Traffic Load ◽  
Traffic Demand ◽  
Channel Bonding ◽  
On Demand ◽  
Multi Agent

In order to meet the ever-increasing traffic demand of Wireless Local Area Networks (WLANs), channel bonding is introduced in IEEE 802.11 standards. Although channel bonding effectively increases the transmission rate, the wider channel reduces the number of non-overlapping channels and is more susceptible to interference. Meanwhile, the traffic load differs from one access point (AP) to another and changes significantly depending on the time of day. Therefore, the primary channel and channel bonding bandwidth should be carefully selected to meet traffic demand and guarantee the performance gain. In this paper, we proposed an On-Demand Channel Bonding (O-DCB) algorithm based on Deep Reinforcement Learning (DRL) for heterogeneous WLANs to reduce transmission delay, where the APs have different channel bonding capabilities. In this problem, the state space is continuous and the action space is discrete. However, the size of action space increases exponentially with the number of APs by using single-agent DRL, which severely affects the learning rate. To accelerate learning, Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is used to train O-DCB. Real traffic traces collected from a campus WLAN are used to train and test O-DCB. Simulation results reveal that the proposed algorithm has good convergence and lower delay than other algorithms.

scholarly journals Analysis and Control of Autonomous Mobility-on-Demand Systems

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Gioele Zardini ◽  
Nicolas Lanzetti ◽  
Marco Pavone ◽  
Emilio Frazzoli
Keyword(s):  
Performance Metrics ◽  
Transportation Systems ◽  
Demand Systems ◽  
Annual Review ◽  
Publication Date ◽  
Systematic Analysis ◽  
On Demand ◽  
Trade Offs ◽  
Autonomous Mobility ◽  
And Control

Challenged by urbanization and increasing travel needs, existing transportation systems need new mobility paradigms. In this article, we present the emerging concept of autonomous mobility-on-demand, whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers. We provide a comprehensive review of methods and tools to model and solve problems related to autonomous mobility-on-demand systems. Specifically, we first identify problem settings for their analysis and control, from both operational and planning perspectives. We then review modeling aspects, including transportation networks, transportation demand, congestion, operational constraints, and interactions with existing infrastructure. Thereafter, we provide a systematic analysis of existing solution methods and performance metrics, highlighting trends and trade-offs. Finally, we present various directions for further research. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Routing and Rebalancing Intermodal Autonomous Mobility-on-Demand Systems in Mixed Traffic

2021 ◽  
pp. 1-13
Author(s):  
Salomon Wollenstein-Betech ◽  
Mauro Salazar ◽  
Arian Houshmand ◽  
Marco Pavone ◽  
Ioannis Ch. Paschalidis ◽  
...  
Keyword(s):  
Demand Systems ◽  
Mixed Traffic ◽  
On Demand ◽  
Autonomous Mobility
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