Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Towards Truly Agent-Based Traffic and Mobility Simulations
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Using Fuzzy Logic in Automated Vehicle Control
IEEE Intelligent Systems
Multiagent Reinforcement Learning for Urban Traffic Control Using Coordination Graphs
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
Autonomous Agents and Multi-Agent Systems
Learning in groups of traffic signals
Engineering Applications of Artificial Intelligence
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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We consider an integrated decision making process of autonomous vehicles in agent-oriented simulation of urban traffic systems. In our approach, the planning process for a vehicle agent is separated into two stages: strategic planning and tactical planning. During the strategic planning stage the vehicle agents constructs the optimal route from source to destination; during the tactical planning stage the operative decisions such as speed regulation and lane change are considered. For strategic planning we modify the stochastic shortest path algorithm with imperfect knowledge about network conditions. For tactical planning we apply distributed multiagent reinforcement learning with other vehicles at the same edge. We present planning algorithms for both stages and demonstrate interconnections between them; an example illustrates how the proposed approach may reduce travel time of vehicle agents in urban traffic.