Multi-agent reinforcement learning (MARL) has witnessed significant progress with the
development of value function factorization methods. It allows optimizing a joint action-value …

Differentiable physics-based simulators have witnessed remarkable success in robot
learning involving contact dynamics, benefiting from their improved accuracy and efficiency …

Successful deployment of multi-agent reinforcement learning often requires agents to adapt
their behaviour. In this work, we discuss the problem of teamwork adaptation in which a …

Team adaptation to new cooperative tasks is a hallmark of human intelligence, which has
yet to be fully realized in learning agents. Previous studies on multi-agent transfer learning …

Cooperative multiagent reinforcement learning (MARL) has attracted significant attention
and has the potential for many real-world applications. Previous arts mainly focus on …

Meta-Reinforcement Learning (MRL) is a promising framework for training agents that can
quickly adapt to new environments and tasks. In this work, we study the MRL problem under …

The evolution of computer science has profoundly impacted decision-making processes
across diverse domains, culminating in the development of artificial intelligence (AI) and …

This thesis studies collaborative learning framework, where a group of agents cooperate to
learn a powerful model from their local data in a distributed or decentralized manner. We …

The rapid growth of data generated by modern applications, such as social media, sensor
networks, and cloud-based databases in various fields, including healthcare, finance, e …