Decision-Support Systems

we suggest a novel reinforcement learning setting that can be used to efficiently generate arbitrary adversarial perturbations using deep multi-agent reinforcement learning.

We used deep multi-agent reinforcement learning to solve an energy systems wargame wherein players simulate IOC decision-making.

This paper introduces Multi-Agent Mujoco, an easily extensible multi-agent benchmark suite for robotic control in continuous action spaces

We demonstrate that, despite its various theoretical shortcomings, Independent PPO (IPPO), a form of independent learning in which each agent simply estimates its local value function, can perform just as well as or better than state-of-the-art joint learning approaches.

We used deep multi-agent reinforcement learning to solve an energy systems wargame wherein players simulate IOC decision-making.