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.

Master Thesis on “Interaction Modeling for Autonomous Driving Using Graph Neural Networks”

The goal of this thesis is to model the interactions among autonomous agents in dense traffic using GNN. In this context, the thesis is expected to include an ablation study to identify which input features (such as velocity, distance to other vehicles, etc.) has the maximum effect on prediction and the effects of changing the graph structure on the interactions among the vehicles.

Energy-Efficiency of Reinforcement Learning

This thesis will investigate the energy consumption of such reinforcement learning algorithms for both training and inference using monitoring capabilities. The goal is to find out how different algorithms compare in performance vs energy consumption on practical applications and if there are ways to reduce energy consumption by trading performance for low computational complexity, for example.

Meta-Learning Embeddings for Reinforcement Learning

Meta-Learning of ‘learning-how-to-learn’ has been immensely popular in the deep learning community in recent years. In this thesis, we will investigate the problem of using meta-learning approaches and ideas to learn latent policy embeddings for use in reinforcement learning. A potential approach for this is hyper networks, ie networks which can generate other networks (see references). The thesis will investigate the application of these approaches and evaluate their usefulness in robot learning and continuous control tasks. Currently, the use of policy embeddings and hyper networks is an active area in research with potential applications to real-world robotics. This is a research-oriented thesis where the student will have the possibility to work on state-of-the-art problems and propose novel methods and algorithms for their use in robot learning.