A Reinforcement Learning Model with Endogenously Determined Learning-Efficiency Parameters: Applications to Route Choice Behavior in Congested Networks

Abstract

This paper proposes a new algorithm for finding disaggregate user equilibria on a congested network where a driver is assumed to be an agent who performs reinforcement learning to get maximal payoff (minimum loss) under limited route information. A reinforcement learning with endogenously determined leaning- efficiency parameters is presented and its relation to the user equilibrium is also explored.