A hybrid learning algorithm for generating multi-agent daily activity plans

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Résumé

This paper proposes a hybrid learning algorithm based on the competing risk model and the cross entropy method for generating complete one-day activity plans for multi-agent traffic simulations. An agent's activity plan generation process is modeled using the Markov decision process. As generating complete activity plans of agents using a reinforcement learning approach is computationally expensive and inefficient, we propose a hybrid method that first estimates the activity type of agents and the scheduled ending time sequences from empirical data based on a competing risk model. The activity plans obtained are then completed by the cross entropy method for the optimal destination choice learning of agents. The performance of the proposed method is compared with the Q-learning algorithm. The numerical result shows that the proposed method generates consistent daily activity plans for multiagent traffic simulations.
langue originaleAnglais
Pages (de - à)959-969
Nombre de pages11
journalJournal of Internet Technology
Volume17
Numéro de publication5
Date de mise en ligne précoce15 juin 2015
Les DOIs
étatPublié - 2016

mots-clés

  • Activity plan generation
  • Cross entropy method
  • Reinforcement learning
  • Simulation
  • multiagent

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