Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning
Journal
Journal of Advanced Transportation
Journal Volume
2021
Date Issued
2021
Author(s)
Abstract
Autonomous buses are becoming increasingly popular and have been widely developed in many countries. However, autonomous buses must learn to navigate the city efficiently to be integrated into public transport systems. Efficient operation of these buses can be achieved by intelligent agents through reinforcement learning. In this study, we investigate the autonomous bus fleet control problem, which appears noisy to the agents owing to random arrivals and incomplete observation of the environment. We propose a multi-agent reinforcement learning method combined with an advanced policy gradient algorithm for this large-scale dynamic optimization problem. An agent-based simulation platform was developed to model the dynamic system of a fixed stop/station loop route, autonomous bus fleet, and passengers. This platform was also applied to assess the performance of the proposed algorithm. The experimental results indicate that the developed algorithm outperforms other reinforcement learning methods in the multi-agent domain. The simulation results also reveal the effectiveness of our proposed algorithm in outperforming the existing scheduled bus system in terms of the bus fleet size and passenger wait times for bus routes with comparatively lesser number of passengers. ? 2021 Sung-Jung Wang and S. K. Jason Chang.
Subjects
Bus transportation
Buses
Fixed platforms
Fleet operations
Intelligent agents
Learning algorithms
Learning systems
Multi agent systems
Reinforcement learning
Agent based simulation
Bus systems
Incomplete observation
Large-scale dynamics
Multi-agent reinforcement learning
Policy gradient
Public transport systems
Reinforcement learning method
Autonomous agents
Type
journal article