In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
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Question: Passengers’ experience is becoming a key metric to evaluate the air transportation system’s performance. Efficient and robust tools to handle airport operations are needed along with a better understanding of passengers’ interests and concerns. Among various airport operations, this paper studies airport gate scheduling for improved passengers’ experience. Three objectives accounting for passengers, aircraft, and operation are presented. Trade-offs between these objectives are analyzed, and a balancing objective function is proposed. The results show that the balanced objective can improve the efficiency of traffic flow in passenger terminals and on ramps, as well as the robustness of gate operations.

Answer: Airport Gate Scheduling for Passengers, Aircraft, and Operation


Question: Abstract: We often encounter situations in which an experimenter wants to find, by sequential experimentation, xmax = arg maxx f(x), where f(x) is a (possibly unknown) function of a well controllable variable x. Taking inspiration from physics and engineering, we have designed a new method to address this problem. In this paper, we first introduce the method in continuous time, and then present two algorithms for use in sequential experiments. Through a series of simulation studies, we show that the method is effective for finding maxima of unknown functions by experimentation, even when the maximum of the functions drifts or when the signal to noise ratio is low.

Answer: Thompson sampling with the online bootstrap


Question: The Particle Swarm Optimization Policy (PSO-P)<lb>has been recently introduced and proven to produce remarkable<lb>results on interacting with academic reinforcement learning<lb>benchmarks in an off-policy, batch-based setting. To further<lb>investigate the properties and feasibility on real-world applica-<lb>tions, this paper investigates PSO-P on the so-called Industrial<lb>Benchmark (IB), a novel reinforcement learning (RL) benchmark<lb>that aims at being realistic by including a variety of aspects found<lb>in industrial applications, like continuous state and action spaces,<lb>a high dimensional, partially observable state space, delayed<lb>effects, and complex stochasticity.<lb>The experimental results of PSO-P on IB are compared to<lb>results of closed-form control policies derived from the model-<lb>based Recurrent Control Neural Network (RCNN) and the<lb>model-free Neural Fitted Q-Iteration (NFQ).<lb>Experiments show that PSO-P is not only of interest for<lb>academic benchmarks, but also for real-world industrial appli-<lb>cations, since it also yielded the best performing policy in our IB<lb>setting. Compared to other well established RL techniques, PSO-<lb>P produced outstanding results in performance and robustness,<lb>requiring only a relatively low amount of effort in finding<lb>adequate parameters or making complex design decisions.

Answer:
Batch Reinforcement Learning on the Industrial Benchmark: First Experiences