Elevator Group Control Using Multiple Reinforcement Learning Agents
Machine Learning
A multiagent reinforcement learning algorithm using extended optimal response
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Pricing in Agent Economies Using Multi-Agent Q-Learning
Autonomous Agents and Multi-Agent Systems
Stock Trading System Using Reinforcement Learning with Cooperative Agents
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Passenger travel behavior model in railway network simulation
Proceedings of the 38th conference on Winter simulation
Spectral feature selection for supervised and unsupervised learning
Proceedings of the 24th international conference on Machine learning
Case-based reinforcement learning for dynamic inventory control in a multi-agent supply-chain system
Expert Systems with Applications: An International Journal
Assigning discounts in a marketing campaign by using reinforcement learning and neural networks
Expert Systems with Applications: An International Journal
Service bid comparisons by fuzzy ranking in open railway market timetabling
Expert Systems with Applications: An International Journal
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Application of reinforcement learning for agent-based production scheduling
Engineering Applications of Artificial Intelligence
Optimal Track Access Rights Allocation for Agent Negotiation in an Open Railway Market
IEEE Transactions on Intelligent Transportation Systems
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Train service timetabling in railway open markets by particle swarm optimisation
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In an open railway access market, the provisions of railway infrastructures and train services are separated and independent. Negotiations between the track owner and train service providers are thus required for the allocation of the track capacity and the formulation of the services timetables, in which each party, i.e. a stakeholder, exhibits intelligence from the previous negotiation experience to obtain the favourable terms and conditions for the track access. In order to analyse the realistic interacting behaviour among the stakeholders in the open railway access market schedule negotiations, intelligent learning capability should be included in the behaviour modelling. This paper presents a reinforcement learning approach on modelling the intelligent negotiation behaviour. The effectiveness of incorporating learning capability in the stakeholder negotiation behaviour is then demonstrated through simulation.