A reinforcement learning scheme for the equilibrium of the in-vehicle route choice problem based on congestion game

Abstract

In this paper, the Bush–Mosteller (B-M) reinforcement learning (RL) scheme is introduced to model the route choice behaviors of the travelers in traffic networks, who aim to seek the optimal travel routes that minimize their individual travel time. The optimal route choice strategy is presented by the Nash equilibrium of the congestion game. By constructing a novel potential function, the congestion game is transformed into the traffic assignment problem (TAP). Then, a distributed algorithm based on B-M RL scheme is devised to solve the TAP. Under some mild conditions, the B-M RL solution method is proven to converge almost surely to the optimal solution of the TAP. A numerical experiment is conducted based on the Nguyen–Dupuis network, the experimental results not only demonstrate the effectiveness of the theoretical analysis, but also show that the B-M RL-based solution method outperforms several existing solution methods.