  Single-agent reinforcement learning algorithms in a multi-agent environment
are inadequate for fostering cooperation. If intelligent agents are to interact
and work together to solve complex problems, methods that counter
non-cooperative behavior are needed to facilitate the training of multiple
agents. This is the goal of cooperative AI. Recent research in adversarial
machine learning, however, shows that models (e.g., image classifiers) can be
easily deceived into making inferior decisions. Meanwhile, an important line of
research in cooperative AI has focused on introducing algorithmic improvements
that accelerate learning of optimally cooperative behavior. We argue that
prominent methods of cooperative AI are exposed to weaknesses analogous to
those studied in prior machine learning research. More specifically, we show
that three algorithms inspired by human-like social intelligence are, in
principle, vulnerable to attacks that exploit weaknesses introduced by
cooperative AI's algorithmic improvements and report experimental findings that
illustrate how these vulnerabilities can be exploited in practice.
