A Survey on the Implementation of Reinforcement Learning on Shared Taxi System

Abstract

Reinforcement learning (RL) is an area of machine learning concerned with how software agents used to take actions in a nature so as to maximize some faith of increasing benefit. In machine learning, the nature is typically formulated as a Markov decision process (MDP), as many of the reinforcement learning algorithms for this context utilize active programming techniques. Taxis plays a important role in modern public transportation networks, especially in countries where public transportation services are still underdeveloped. Taxi drivers currently rely on a simple first come-first serve approach, with a high coefficient of luck controlling their profit Problems arising from the current system are low taxi utilization, long passenger waiting times, road safety issues, and traffic congestion. In this paper, we present a system that improves the shared taxi service by benefiting both the taxi drivers and the passengers. This study proposed a various reinforcement learning method for a shared-taxi system. The present system proposes a solution in the form of a good, efficient and economical transportation service.