Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

Network-based modeling of complex systems and data using the language of graphs has
become an essential topic across a range of different disciplines. Arguably, this graph-based …

The cornerstone underpinning deep learning is the guarantee that gradient descent on an
objective converges to local minima. Unfortunately, this guarantee fails in settings, such as …

Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of
agents, for example labeling them 'winner'and 'loser'. If the game is approximately transitive …

This is an elementary introduction to the Hodge Laplacian on a graph, a higher-order
generalization of the graph Laplacian. We will discuss basic properties including …

Most existing results about last-iterate convergence of learning dynamics are limited to two-
player zero-sum games, and only apply under rigid assumptions about what dynamics the …

We introduce α-Rank, a principled evolutionary dynamics methodology, for the evaluation
and ranking of agents in large-scale multi-agent interactions, grounded in a novel dynamical …

Promoting behavioural diversity is critical for solving games with non-transitive dynamics
where strategic cycles exist, and there is no consistent winner (eg, Rock-Paper-Scissors) …

Abstract Policy-Space Response Oracles (PSRO) is an influential algorithm framework for
approximating a Nash Equilibrium (NE) in multi-agent non-transitive games. Many previous …

Progress in machine learning is measured by careful evaluation on problems of outstanding
common interest. However, the proliferation of benchmark suites and environments …