My academic interests lie in the field of reinforcement learning and approaches to improve its practical feasibility. I believe this can be done by making use of human guidance in various forms, along with other approaches to improve the agent's context-awareness. In the future, I also intend to explore neuroevolutionary approaches to integrate intra-life (aka learning) and inter-life (aka evolutionary) adaptation as a step towards automating the design of artificial agents.
News & Updates
Happy to annouce that our paper "Controlled Diversity with Preference: Towards Learning a Diverse Set of Desired Skills", led by my PhD student, Maxence Hussonnois, has been nominated for the best student paper award at AAMAS 2023!