2 citations · 5 across the 6 of their papers we have counts for
6 papers
Applications of deep reinforcement learning to urban transit network design
Andrew Holliday
This thesis concerns the use of reinforcement learning to train neural networks to aid in the design of public transit networks. The Transit Network Design Problem (TNDP) is an opt…
Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning
Andrew Holliday, Ahmed El-Geneidy, Gregory Dudek
Planning a network of public transit routes is a challenging optimization problem. Metaheuristic algorithms search through the space of possible transit networks by applying heuris…
A Neural-Evolutionary Algorithm for Autonomous Transit Network Design
Andrew Holliday, Gregory Dudek
Planning a public transit network is a challenging optimization problem, but essential in order to realize the benefits of autonomous buses. We propose a novel algorithm for planni…
Uncertainty-aware hybrid paradigm of nonlinear MPC and model-based RL for offroad navigation: Exploration of transformers in the predictive model
Faraz Lotfi, Khalil Virji, Farnoosh Faraji +4
In this paper, we investigate a hybrid scheme that combines nonlinear model predictive control (MPC) and model-based reinforcement learning (RL) for navigation planning of an auton…
Neural Bee Colony Optimization: A Case Study in Public Transit Network Design
Andrew Holliday, Gregory Dudek
In this work we explore the combination of metaheuristics and learned neural network solvers for combinatorial optimization. We do this in the context of the transit network design…
Scale-Robust Localization Using General Object Landmarks
Andrew Holliday, Gregory Dudek
Visual localization under large changes in scale is an important capability in many robotic mapping applications, such as localizing at low altitudes in maps built at high altitude…