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 […]

The aim of this thesis is based on consecutive action planning for robot manipulation and will focus on implementation and improving of a recently presented visual action planning of complex manipulation tasks

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.