Dynamic Target Time Management with Reinforcement Learning: A case study on Zurich short-haul regulated arrivals

This Master Thesis investigates the possible improvements to the Target Time Management concept to optimize the arrival flows for SWISS International Airlines. The aim is to improve operational performance based on the current model used, as well as prove that Target Time Management constitutes a valuable system to improve operations in a...

What are the implications of Curriculum Learning strategy on IRL methods?: Investigating Inverse Reinforcement Learning from Human Behavior

Inverse Reinforcement Learning (IRL) is a subfield of Reinforcement Learning (RL) that focuses on recovering the reward function using expert demonstrations. In the field of IRL, Adversarial IRL (AIRL) is a promising algorithm that is postulated to recover non-linear rewards in environments with unknown dynamics. This study investigates the...

Incorporating climate change in I&M planning for engineering structures - A Deep Reinforcement Learning Framework

In the last decades, climate change is causing our environment to change rapidly, unprecedented in recent history. Civil engineering structures are dependent on the deteriorating environment they are situated in. Changes can cause an increase in loading due to, for example, extreme weather events or alter the structure’s resistance by, for...

Improving a Reinforcement Learning Negotiating Agent’s Performance by Extracting Information from the Opponent’s Sequence of Offers

With the prospects of decentralized multi-agent systems becoming more prevalent in daily life, automated negotiation agents have made their place in these collaborative settings. They are an approach to promote communication between the agents in reaching solutions that are better for all involved.<br/><br/>Recent literature has shown great...

End-to-End Hierarchical Reinforcement Learning for Adaptive Flight Control: A method for model-independent control through Proximal Policy Optimization with learned Options

Aircraft with disruptive designs have no high-fidelity and accurate flight models. At the same time, developing models for stochastic phenomena for traditional aircraft configurations are costly, and classical control methods cannot operate beyond the predefined operation points or adapt to unexpected changes to the aircraft. The Proximal Policy...