Matrix games like Prisoner's Dilemma have guided research on social dilemmas
for decades. However, they necessarily treat the choice to cooperate or defect
as an atomic action. In real-world social dilemmas these choices are temporally
extended. Cooperativeness is a property that applies to policies, not
elementary actions. We introduce sequential social dilemmas that share the
mixed incentive structure of matrix game social dilemmas but also require
agents to learn policies that implement their strategic intentions. We analyze
the dynamics of policies learned by multiple self-interested independent
learning agents, each using its own deep Q-network, on two Markov games we
introduce here: 1. a fruit Gathering game and 2. a Wolfpack hunting game. We
characterize how learned behavior in each domain changes as a function of
environmental factors including resource abundance. Our experiments show how
conflict can emerge from competition over shared resources and shed light on
how the sequential nature of real world social dilemmas affects cooperation.

本文提出了顺序社会困境概念，以两种引入的马尔可夫博弈为例，分析了多个自利的独立学习智能体使用自己的 Deep Q-Networks 所学习的策略，同时展示了竞争如何导致冲突并阐明了真实世界社会困境的顺序性质如何影响合作。