Communication improves the efficiency and convergence of multiagent learning. Existing
study of agent communication has been limited on predefined fixed connections. While an …

Learning to act in a multiagent environment is a challenging problem. Optimal behavior for
one agent depends upon the behavior of the other agents, which are learning as well …

In this paper we advocate a paradigm of socially guided machine learning, designing agents
that take better advantage of the situated aspects of learning. We augmented a standard …

Grouping is ubiquitous in natural systems and is essential for promoting efficiency in team
coordination. This paper proposes a novel formulation of Group-oriented Multi-Agent …

This work considers the problem of learning cooperative policies in complex, partially
observable domains without explicit communication. We extend three classes of single …

Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with
other agents through autonomous exploration of the environment. However, learning a …

Infants are experts at playing, with an amazing ability to generate novel structured behaviors
in unstructured environments that lack clear extrinsic reward signals. We seek to …

Seamlessly interacting with humans or robots is hard because these agents are non-
stationary. They update their policy in response to the ego agent's behavior, and the ego …

Many complex multi-agent systems such as robot swarms control and autonomous vehicle
coordination can be modeled as Multi-Agent Reinforcement Learning (MARL) tasks. QMIX, a …

The main challenge of multiagent reinforcement learning is the difficulty of learning useful
policies in the presence of other simultaneously learning agents whose changing behaviors …