Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

Multi-agent systems can be used to address problems in a variety of domains, including
robotics, distributed control, telecommunications, and economics. The complexity of many …

The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …

Increasingly, algorithms are supplanting human decision-makers in pricing goods and
services. To analyze the possible consequences, we study experimentally the behavior of …

Matrix games like Prisoner's Dilemma have guided research on social dilemmas for
decades. However, they necessarily treat the choice to cooperate or defect as an atomic …

Despite the recent advances of deep reinforcement learning (DRL), agents trained by DRL
tend to be brittle and sensitive to the training environment, especially in the multi-agent …

Graph Neural Networks (GNNs) have been widely used for the representation learning of
various structured graph data, typically through message passing among nodes by …

The key challenge in multiagent learning is learning a best response to the behaviour of
other agents, which may be non-stationary: if the other agents adapt their strategy as well …

Imitation learning algorithms can be used to learn a policy from expert demonstrations
without access to a reward signal. However, most existing approaches are not applicable in …

Existing smart grid security research investigates different attack techniques and cascading
failures from the attackers' viewpoints, while the defenders' or the operators' protection …