Master thesis on “Development of data-driven driver model”

Supervisor: Prof. Ville Kyrki (ville.kyrki@aalto.fi) Advisor: Daulet Baimukashev  (daulet.baimukashev@aalto.fi), Shoaib Azam (shoaib.azam@aalto.fi) Keywords: imitation learning, autonomous driving Data-driven driver models are superior to rule-based models in interactive multi-agent scenarios where it is essential to consider agents’ behavior. For example, humans have diverse driving styles as aggressive, neutral, or defensive [1] and it is challenging to […]

Semantic map generation in SUMO

This project aims to extend the functionality of the SUMO simulator with suitable software packages which generate semantic representations and control the vehicles using low-level control actions. This enables integration of data-driven vehicle models.

Master Thesis on “Interactive Bayesian Multiobjective Evolutionary Optimization in Reinforcement Learning Problems with Conflicting Reward Functions”

In many real-world problems, there are multiple conflicting objective functions that need to be optimized simultaneously. For example, an investment company wants to create an optimum portfolio of stocks to maximize profits and minimize risk simultaneously. However, most reinforcement learning (RL) problems do not explicitly consider the tradeoff between multiple conflicting reward functions and assume a scalarized single objective reward function to be optimized. Multiobjective evolutionary optimization algorithms (MOEAs) can be used to find Pareto optimal policies by considering multiple reward functions as objectives.