This paper surveys the field of deep multiagent reinforcement learning (RL). The
combination of deep neural networks with RL has gained increased traction in recent years …

The ability to cooperate through language is a defining feature of humans. As the
perceptual, motory and planning capabilities of deep artificial networks increase …

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 …

Communication is an effective mechanism for coordinating the behaviors of multiple agents,
broadening their views of the environment, and to support their collaborations. In the field of …

We propose a unified mechanism for achieving coordination and communication in Multi-
Agent Reinforcement Learning (MARL), through rewarding agents for having causal …

From the early days of computing, games have been important testbeds for studying how
well machines can do sophisticated decision making. In recent years, machine learning has …

OpenSpiel is a collection of environments and algorithms for research in general
reinforcement learning and search/planning in games. OpenSpiel supports n-player (single …

Deep reinforcement learning has gathered much attention recently. Impressive results were
achieved in activities as diverse as autonomous driving, game playing, molecular …

This paper introduces a new framework for open-domain question answering in which the
retriever and the reader iteratively interact with each other. The framework is agnostic to the …

Emergent communication aims for a better understanding of human language evolution and
building more efficient representations. We posit that reaching these goals will require …