We propose the first, to our knowledge, loss function for approximate Nash equilibria of
normal-form games that is amenable to unbiased Monte Carlo estimation. This construction …

Pseudo-games are a natural and well-known generalization of normal-form games, in which
the actions taken by each player affect not only the other players' payoffs, as in games, but …

The behaviour of multi-agent learning in competitive settings is often considered under the
restrictive assumption of a zero-sum game. Only under this strict requirement is the …

Rating strategies in a game is an important area of research in game theory and artificial
intelligence, and can be applied to any real-world competitive or cooperative setting …

Understanding the impact of exploration on the behaviour of multi-agent learning has, so far,
benefited from the restriction to potential, or network zero-sum games in which convergence …

In game theory, a game refers to a model of interaction among rational decision-makers or
players, making choices with the goal of achieving their individual objectives. Understanding …

We present new data structures for representing symmetric normal-form games. These data
structures are optimized for efficiently computing the expected utility of each unilateral pure …

Nash Equilibrium (NE) is the canonical solution concept of game theory, which provides an
elegant tool to understand the rationalities. Though mixed strategy NE exists in any game …

Multi-agent learning algorithms have been shown to display complex, unstable behaviours
in a wide array of games. In fact, previous works indicate that convergent behaviours are …

Abstract The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-
agent learning ranging from computing approximations to fundamental concepts in game …