| Reinforcement Learning (RL) is a subdomain of machine learning that has developed rapidly in recent years and has become increasingly popular. In reinforcement learning an agent learns from experience using a scalar reward signal, in contrast to learning from examples of labelled data as it is done in supervised machine learning. The agent interacts with an environment by taking actions that influence the environment and thus the observations that are the agent’s input.What is often learned by a RL agent is the expected return of taking a certain action in a certain state. There are, however, also algorithms that try to capture more information than just the expected value, i.e. higher order moments or even full distributions. Learning the variance for example has been used in designing risk-sensitive agents.For this thesis you would search for such methods in existing literature, choose some of them (the number depends on the amount of work each one entails) and implement and compare them amongst each other and, if time allows it, with non-distributional variants of the same underlying algorithms. Criteria will include learning efficiency and computational efficiency but you should add your own as well. |
| Planned steps with estimated time· Reinforcement Learning foundation (depending on level of knowledge)Literature review and understanding (45 days)Implementation of chosen algorithms (20 days).Evaluation and comparison of implemented algorithms (15 days)Writing of the thesis (45 days) |
| Prerequisites:Interest, motivation in machine learningKnowledge and experience with RL are desirable, but not strictly necessary. This will be reflected in the length of the initial learning phase.Programming skills (preferably in Python) Proficient use of scientific work |
| Begin and duration:Anytime, 6 months |
| Co-supervisors: Felix Grün, M. Sc.Tel.: +49 208 88254 875, felix.gruen@hs-ruhrwest.de Supervisors: Prof. Dr. Ioannis Iossifidis, +49 208 88254806 iossifidis@hs-ruhrwest.de |