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 …

Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous
disciplines, including game theory, economics, social sciences, and evolutionary biology …

Value decomposition recently injects vigorous vitality into multi-agent actor-critic methods.
However, existing decomposed actor-critic methods cannot guarantee the convergence of …

Recent research on reinforcement learning in pure-conflict and pure-common interest
games has emphasized the importance of population heterogeneity. In contrast, studies of …

The challenge of developing powerful and general Reinforcement Learning (RL) agents has
received increasing attention in recent years. Much of this effort has focused on the single …

In this work, we ask for and answer what makes classical temporal-difference reinforcement
learning with ϵ-greedy strategies cooperative. Cooperating in social dilemma situations is …

Advances in artificial intelligence often stem from the development of new environments that
abstract real-world situations into a form where research can be done conveniently. This …

Recent advances in deep reinforcement learning (RL) have led to considerable progress in
many 2-player zero-sum games, such as Go, Poker and Starcraft. The purely adversarial …

Society is characterized by the presence of a variety of social norms: collective patterns of
sanctioning that can prevent miscoordination and free-riding. Inspired by this, we aim to …

The usage of automated learning agents is becoming increasingly prevalent in many online
economic applications such as online auctions and automated trading. Motivated by such …