  To achieve general intelligence, agents must learn how to interact with
others in a shared environment: this is the challenge of multiagent
reinforcement learning (MARL). The simplest form is independent reinforcement
learning (InRL), where each agent treats its experience as part of its
(non-stationary) environment. In this paper, we first observe that policies
learned using InRL can overfit to the other agents' policies during training,
failing to sufficiently generalize during execution. We introduce a new metric,
joint-policy correlation, to quantify this effect. We describe an algorithm for
general MARL, based on approximate best responses to mixtures of policies
generated using deep reinforcement learning, and empirical game-theoretic
analysis to compute meta-strategies for policy selection. The algorithm
generalizes previous ones such as InRL, iterated best response, double oracle,
and fictitious play. Then, we present a scalable implementation which reduces
the memory requirement using decoupled meta-solvers. Finally, we demonstrate
the generality of the resulting policies in two partially observable settings:
gridworld coordination games and poker.
