Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

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 …

While training a machine learning model using multiple workers, each of which collects data
from its own data source, it would be useful when the data collected from different workers …

Optimizing distributed learning systems is an art of balancing between computation and
communication. There have been two lines of research that try to deal with slower …

In this paper, we propose the problem of collaborative perception, where robots can
combine their local observations with those of neighboring agents in a learnable way to …

Modelling and exploiting teammates' policies in cooperative multi-agent systems have long
been an interest and also a big challenge for the reinforcement learning (RL) community …

The implementation of optimal power flow (OPF) methods to perform voltage and power flow
regulation in electric networks is generally believed to require extensive communication. We …

The ever-growing requirement for reliability and quality of power supply suggests to enable
self-healing features of smart distribution network using intelligent communication and …

Large-scale multiagent reinforcement learning requires huge computation and space costs,
and the too-long execution process makes it hard to train policies for agents. This work …

Machine learning (ML) is increasingly deployed in real world contexts, supplying actionable
insights and forming the basis of automated decision-making systems. While issues …