Inverse Optimal Planning for Air Traffic Control

We envision a system that concisely describes the rules of air traffic control, assists human operators and supports dense autonomous air traffic around commercial airports. We develop a method to learn the rules of air traffic control from real data as a cost function via maximum entropy inverse reinforcement learning. This cost function is used as a penalty for a search-based motion planning method that discretizes both the control and the state space. We illustrate the methodology by showing that our approach can learn to imitate the airport arrival routes and separation rules of dense commercial air traffic. The resulting trajectories are shown to be safe, feasible, and efficient

Investigating hybrids of evolution and learning for real-parameter optimization

In recent years, more and more advanced techniques have been developed in the field of hybridizing of evolution and learning, this means that more applications with these techniques can benefit from this progress. One example of these advanced techniques is the Learnable Evolution Model (LEM), which adopts learning as a guide for the general evolutionary search. Despite this trend and the progress in LEM, there are still many ideas and attempts which deserve further investigations and tests. For this purpose, this thesis has developed a number of new algorithms attempting to combine more learning algorithms with evolution in different ways. With these developments, we expect to understand the effects and relations between evolution and learning, and also achieve better performances in solving complex problems. The machine learning algorithms combined into the standard Genetic Algorithm (GA) are the supervised learning method k-nearest-neighbors (KNN), the Entropy-Based Discretization (ED) method, and the decision tree learning algorithm ID3. We test these algorithms on various real-parameter function optimization problems, especially the functions in the special session on CEC 2005 real-parameter function optimization. Additionally, a medical cancer chemotherapy treatment problem is solved in this thesis by some of our hybrid algorithms. The performances of these algorithms are compared with standard genetic algorithms and other well-known contemporary evolution and learning hybrid algorithms. Some of them are the CovarianceMatrix Adaptation Evolution Strategies (CMAES), and variants of the Estimation of Distribution Algorithms (EDA). Some important results have been derived from our experiments on these developed algorithms. Among them, we found that even some very simple learning methods hybridized properly with evolution procedure can provide significant performance improvement; and when more complex learning algorithms are incorporated with evolution, the resulting algorithms are very promising and compete very well against the state of the art hybrid algorithms both in well-defined real-parameter function optimization problems and a practical evaluation-expensive problem

Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces

Policy optimization methods have shown great promise in solving complex reinforcement and imitation learning tasks. While model-free methods are broadly applicable, they often require many samples to optimize complex policies. Model-based methods greatly improve sample-efficiency but at the cost of poor generalization, requiring a carefully handcrafted model of the system dynamics for each task. Recently, hybrid methods have been successful in trading off applicability for improved sample-complexity. However, these have been limited to continuous action spaces. In this work, we present a new hybrid method based on an approximation of the dynamics as an expectation over the next state under the current policy. This relaxation allows us to derive a novel hybrid policy gradient estimator, combining score function and pathwise derivative estimators, that is applicable to discrete action spaces. We show significant gains in sample complexity, ranging between $1.7$ and $25\times$, when learning parameterized policies on Cart Pole, Acrobot, Mountain Car and Hand Mass. Our method is applicable to both discrete and continuous action spaces, when competing pathwise methods are limited to the latter.Comment: In AAAI 2018 proceeding

Optimal Aerodynamic Design of a Transonic Centrifugal Turbine Stage for Organic Rankine Cycle Applications

This paper presents the results of the application of a shape-optimization technique to the design of the stator and the rotor of a centrifugal turbine conceived for Organic Rankine Cycle (ORC) applications. Centrifugal turbines have the potential to compete with axial or radial-inflow turbines in a relevant range of applications, and are now receiving scientific as well as industrial recognition. However, the non-conventional character of the centrifugal turbine layout, combined with the typical effects induced by the use of organic fluids, leads to challenging design difficulties. For this reason, the design of optimal blades for centrifugal ORC turbines demands the application of high-fidelity computational tools. In this work, the optimal aerodynamic design is achieved by applying a non-intrusive, gradient-free, CFD-based method implemented in the in-house software FORMA (Fluid-dynamic Opti-mizeR for turboMachinery Aerofoils), specifically developed for the shape optimization of turbomachinery profiles. FORMA was applied to optimize the shape of the stator and the rotor of a transonic centrifugal turbine stage, which exhibits a significant radial effect, high aerodynamic loading, and severe non-ideal gas effects. The optimization of the single blade rows allows improving considerably the stage performance, with respect to a baseline geometric configuration constructed with classical aerodynamic methods. Furthermore, time-resolved simulations of the coupled stator-rotor configuration shows that the optimization allows to reduce considerably the unsteady stator-rotor interaction and, thus, the aerodynamic forcing acting on the blades

Towards Recommendations for Value Sensitive Sustainable Consumption

Excessive consumption can strain natural resources, harm the environment, and widen societal gaps. While adopting a more sustainable lifestyle means making significant changes and potentially compromising personal desires, balancing sustainability with personal values poses a complex challenge. This article delves into designing recommender systems using neural networks and genetic algorithms, aiming to assist consumers in shopping sustainably without disregarding their individual preferences. We approach the search for good recommendations as a problem involving multiple objectives, representing diverse sustainability goals and personal values. While using a synthetic historical dataset based on real-world sources, our evaluations reveal substantial environmental benefits without demanding drastic personal sacrifices, even if consumers accept only a fraction of the recommendations