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

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 multi-agent traffic simulations.