Speaker will be Tereza Siváková.

The research studies optimal decision-making with the focus on preferences quantified for fully probabilistic design (FPD). FPD models the closed DM loop and the agent’s preferences by joint probability densities (pds). There is a preference-elicitation (PE) principle, which maps the agent’s model of the state transitions and its incompletely expressed wishes on an ideal pd quantifying them. This research also studies preferences targeting actions and contradicting preferences.

Speaker will be Marko Ruman.

Deep reinforcement learning has shown an ability to achieve super-human performance in solving complex reinforcement learning tasks only from raw-pixels. However, it fails to reuse knowledge from previously learnt tasks to solve new, unseen ones. To generalize and reuse knowledge is one of the fundamental requirements for creating a truly intelligent agent. The work summarizes the problem of transfer learning in reinforcement learning tasks and offers a method for one-to-one task transfer learning.

Marko Ruman složil státní doktorskou zkoušku s vyznamenáním (obor: Matematické inženýrství, FJFI ČVUT) a úspešně představil teze své práce na téma "Transfer Learning in Reinforcement Learning".

Blahopřejeme!

The aim of this event concluding the second year of COST Action European Network for Game Theory GAMENET is to present, discuss and share the progress on research made in the working groups of GAMENET: