reinforcement learning

A deep reinforcement learning approach for the asynchronous dynamic vehicle dispatching problem

This paper addresses the asynchronous dynamic vehicle dispatching problem (DVDP), in which vehicle assignments must be made in real time as requests arise or vehicles become available. Unlike traditional synchronous dispatching, the asynchronous DVDP …

Trajectory modeling via random utility inverse reinforcement learning

We consider the problem of modeling trajectories of drivers in a road network from the perspective of inverse reinforcement learning. Cars are detected by sensors placed on sparsely distributed points on the street network of a city. As rational …

A reinforcement learning approach to the stochastic cutting stock problem

We propose a formulation of the stochastic cutting stock problem as a discounted infinite-horizon Markov decision process. At each decision epoch, given current inventory of items, an agent chooses in which patterns to cut objects in stock in …

Aplicação de aprendizado por reforço ao problema de corte de estoque estocástico

Propõe-se uma formulação do problema de corte de estoque estocástico como um processo de decisão markoviano de horizonte infinito descontado. Em cada época de decisão deve-se escolher as quantidades de itens a serem cortados em antecipação à demanda …