ELLIS Delft Research theme - Multiagent Systems & Robust and Adversarial ML

Topics: distributed learning, multi-agent reinforcement learning (MARL), robust optimization, adversarial attacks, game theory, coordination

Multi-agent systems (MAS) and multi-agent learning (MAL) have gained significant attention due to their potential in addressing complex problems that cannot be solved by a single agent. These systems are composed of multiple autonomous agents that interact with their environment and each other to achieve their objectives. However, ensuring coordination and cooperation among multiple agents in complex environments remains a challenge with applications in robotics, energy networks, cybersecurity, and transportation, among others. Additionally, balancing exploration and exploitation, dealing with non-stationarity and partial observability, and lack of interpretability in some models are challenges that require new approaches, including game theory, deep reinforcement learning, communication protocols, and insights from psychology and social sciences