Self-adaptive and multi-agent reinforcement learning in route guidance system

Zolfpour-Arokhlo, Mortaza; Selamat, Ali; Hashim, Siti Zaiton Mohd

doi:10.1109/mysec.2011.6140702

Cited by 2 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Value (5,10) −112.729 (5,4) −112.9003 (4,3) −106.3563 (4,9) −107.0302 (3,8) −97.5428 (3,2) −100.6263 (8,13) −79.9226 (10,15) −105.3436 (15,14) −96.4487 (15,20) −98.3531 (14,13) −79.9226 (13,12) −82.4741 (13,18) −69.2806 (18,17) 0 −87.8625 (6,11) −87.7966 (7,8) −87.5428 (7,12) −92.4741 (8,13) −79.9226 (8,9) −79.0302 (9,14) 0 (11,12) −82.4741 (11,16) −94.6919 (12,13) −79.9226 (12,17) −86.1356 (13,14) 0…”

Section: ( )mentioning

confidence: 99%

“…Recently, multiagent reinforcement learning has been proposed to find the best and shortest path between the origin and the destination. Some studies treat each intersection as one agent, which needs a large amount of information interaction between traffic intersections to find the optimal path [10] while more studies cast each intersection as the state and take each link as the action in the model, which could deal with the road networks on the whole [11,12]. Thus our proposed -learning adopts the latter method, treating the intersections as states in the model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A CooperativeQ-Learning Path Planning Algorithm for Origin-Destination Pairs in Urban Road Networks

Zhang

Peng

et al. 2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

As an important part of intelligent transportation systems, path planning algorithms have been extensively studied in the literature. Most of existing studies are focused on the global optimization of paths to find the optimal path between Origin-Destination (OD) pairs. However, in urban road networks, the optimal path may not be always available when some unknown emergent events occur on the path. Thus a more practical method is to calculate several suboptimal paths instead of finding only one optimal path. In this paper, a cooperativeQ-learning path planning algorithm is proposed to seek a suboptimal multipath set for OD pairs in urban road networks. The road model is abstracted to the form thatQ-learning can be applied firstly. Then the gray prediction algorithm is combined intoQ-learning to find the suboptimal paths with reliable constraints. Simulation results are provided to show the effectiveness of the proposed algorithm.

show abstract

Section: ( )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A CooperativeQ-Learning Path Planning Algorithm for Origin-Destination Pairs in Urban Road Networks

Zhang

Peng

et al. 2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…A number of research projects have been done in brain-inspired application [12,13,14]. However, there are very limited works done for network storage domain.…”

Section: Introductionmentioning

confidence: 99%

Development of Intelligent Network Storage System with Adaptive Decision-Making

Sahagun¹,

Ren²,

Beng³

et al. 2012

IJACT

View full text Add to dashboard Cite

An enormous amount of digitization of content has led to extreme demands on storage systems. Multitier storage systems built with parallel distributed file-system enhance their scalability and I/O performance, while achieving cost-effectiveness in handling large data. However, these storage systems are not flexible enough to adapt to the complexity and dynamic environment of data. Designing a storage system to be more intelligent is a proven technique to overcome the challenges of managing complex data in a dynamic environment and hence, this paper proposes an Intelligent Network Storage System (INSS) with adaptive decision-making capability. An intelligent agent is introduced for autotiering and it can dynamically decide which storage tier data can be relocated based on their recent access patterns. The intelligent agent's design and implementation were inspired by the human brain and consequently, the functions from each of brain's five core regions were developed in INSS prototype. The intelligent agent utilizes Reinforcement Learning (RL) which guides the system itself to learn the optimal policies in relocating data. The prototype was built on a test bed with Lustre: A SAN file system for Linux with one metadata server and 3 storage nodes. With its adaptive and real-time decisionmaking capability, the policies optimized by RL were evaluated and the analysis has shown to achieve a high degree of accuracy with no performance penalty.

show abstract

Self-adaptive and multi-agent reinforcement learning in route guidance system

Cited by 2 publications

References 18 publications

A CooperativeQ-Learning Path Planning Algorithm for Origin-Destination Pairs in Urban Road Networks

A CooperativeQ-Learning Path Planning Algorithm for Origin-Destination Pairs in Urban Road Networks

Development of Intelligent Network Storage System with Adaptive Decision-Making

Contact Info

Product

Resources

About