Modeling of route planning system based on Q value-based dynamic programming with multi-agent reinforcement learning algorithms

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

doi:10.1016/j.engappai.2014.01.001

Cited by 78 publications

(30 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this context, their algorithm outperforms similar algorithms such as Q-learning, real-time adaptive learning, and fixed timing plans when considering average delay, number of stops and vehicular emissions. In addition, Zolfpour-Arokhlo et al [20] apply a multi-agent reinforcement learning algorithm for obtaining a route planning system. They consider environment features such as weather, traffic, road safety and fuel capacity.…”

Section: Learning Algorithms In Abssmentioning

confidence: 99%

ATABS: A technique for automatically training agent-based simulators

García‐Magariño

Palacios-Navarro

2016

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

Section: Learning Algorithms In Abssmentioning

confidence: 99%

ATABS: A technique for automatically training agent-based simulators

García‐Magariño

Palacios-Navarro

2016

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

“…Recently, Multi-Agent Systems (MAS) and Reinforcement Learning (RL) have been integrated and applied to the field of traffic management, such as traffic control [29]- [30] and route planning [31]- [32]. With the advantages of both MAS and RL, Multi-Agent Reinforcement Learning (MARL) was introduced for TA [33].…”

Section: Introductionmentioning

confidence: 99%

A Distributed Assignment Method for Dynamic Traffic Assignment Using Heterogeneous-Adviser Based Multi-Agent Reinforcement Learning

Pan

Chen

et al. 2020

IEEE Access

View full text Add to dashboard Cite

The Dynamic Traffic Assignment (DTA) is one of the important measures to alleviate urban network traffic congestion. The congestions are usually caused by stochastic traffic demands, which are generally unassignable from time dimension in the real-world but are assumed to be assignable in existing DTA methods (i.e. real-time travel demands). In this paper, a distributed DTA method for preventing urban network traffic congestion caused by stochastic real-time travel demands by improving Multi-Agent Reinforcement Learning (MARL). A team structure, which consists of decision-makers and advisers, is designed to learn parallelly in realistic DTA tasks. To reduce the size of the solution space adaptively, the dynamic critical values advised by adviser agents are adopted as constraints for the strategy space of decision-makers (i.e. main agents). A collaborative heterogeneous-adviser mechanism is designed to avoid deviation of guidance. To enhance the adaptability of DTA to the changeable external environment, the mixed strategy concept is introduced to improve the decision-making process of main agents. The respective mapping mechanisms are designed to define adaptive learning rates to improve the sensitivity of MARL. The Sioux Falls (SF) network is established as a test platform via a Dynamic Network Loading (DNL). The effectiveness of the suggested DTA method is assessed through numerical simulations SF network. Under the influence of the scenario with stochastic real-time travel demands, the results show that the proposed method outperforms in terms of the throughput of the network and the individual average travel time among the overall network. Additionally, the ability of the proposed method in response to the external environment rapidly has also been demonstrated. Adopting the suggested method can improve the state of the art to assign stochastic real-time travel demands dynamically and to avoid potential traffic congestion fundamentally. INDEX TERMS dynamic traffic assignment, intelligent transportation system, multi-agent system, reinforcement learning, multi-agent reinforcement learning, numerical simulation.

show abstract

“…The decision-making architecture consists of three layers: (i) Global Route Planning (GRP), (ii) Local Path Planning (LPP), and (iii) Feedback Control (FC). The GRP planning layer assigns optimal waypoints using dynamic programming (DP) [14], [15]. The GRP must rely on cloud and stored database information to define and sequence waypoints beyond onboard sensor range (line-of-sight).…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Approach for Autonomous Motion Planning and Control in Off-Road Driving Scenarios

Rastgoftar

Zhang

Atkins

2018

2018 Annual American Control Conference (ACC)

View full text Add to dashboard Cite

This paper presents a novel data-driven approach for vehicle motion planning and control in off-road driving scenarios. For autonomous off-road driving, environmental conditions impact terrain traversability as a function of weather, surface composition, and slope. Geographical information system (GIS) and National Centers for Environmental Information datasets are processed to provide this information for interactive planning and control system elements. A top-level global route planner (GRP) defines optimal waypoints using dynamic programming (DP). A local path planner (LPP) computes a desired trajectory between waypoints such that infeasible control states and collisions with obstacles are avoided. The LPP also updates the GRP with real-time sensing and control data. A low-level feedback controller applies feedback linearization to asymptotically track the specified LPP trajectory. Autonomous driving simulation results are presented for traversal of terrains in Oregon and Indiana case studies.

show abstract

Modeling of route planning system based on Q value-based dynamic programming with multi-agent reinforcement learning algorithms

Cited by 78 publications

References 36 publications

ATABS: A technique for automatically training agent-based simulators

ATABS: A technique for automatically training agent-based simulators

A Distributed Assignment Method for Dynamic Traffic Assignment Using Heterogeneous-Adviser Based Multi-Agent Reinforcement Learning

A Data-Driven Approach for Autonomous Motion Planning and Control in Off-Road Driving Scenarios

Contact Info

Product

Resources

About