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In this work, we propose a novel memory-based multi-agent meta-learning architecture and learning procedure that allows for learning of a shared communication policy that enables the emergence of rapid adaptation to new and unseen environments by learning to learn learning algorithms through communication. Behavior, adaptation and learning to adapt emerges from the interactions of homogeneous experts inside a single agent. The proposed architecture should allow for generalization beyond the level seen in existing methods, in part due to the use of a single policy shared by all experts within the agent as well as the inherent modularity of Badger.
We discuss the problem of learning collaborative behaviour through communication in multi-agent systems using deep reinforcement learning. A connectivity-driven communication (CDC) algorithm is proposed to address three key aspects: what agents to in
The ability of modeling the other agents, such as understanding their intentions and skills, is essential to an agents interactions with other agents. Conventional agent modeling relies on passive observation from demonstrations. In this work, we pro
In reinforcement learning, agents learn by performing actions and observing their outcomes. Sometimes, it is desirable for a human operator to textit{interrupt} an agent in order to prevent dangerous situations from happening. Yet, as part of their l
Most of the prior work on multi-agent reinforcement learning (MARL) achieves optimal collaboration by directly controlling the agents to maximize a common reward. In this paper, we aim to address this from a different angle. In particular, we conside
We propose ScheduleNet, a RL-based real-time scheduler, that can solve various types of multi-agent scheduling problems. We formulate these problems as a semi-MDP with episodic reward (makespan) and learn ScheduleNet, a decentralized decision-making