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

Nash equilibrium is one of the most influential solution concepts in game theory. With the
development of computer science and artificial intelligence, there is an increasing demand …

There has been a resurgence of interest in multiagent reinforcement learning (MARL), due
partly to the recent success of deep neural networks. The simplest form of MARL is …

OpenSpiel is a collection of environments and algorithms for research in general
reinforcement learning and search/planning in games. OpenSpiel supports n-player (single …

In the empirical approach to game-theoretic analysis (EGTA), the model of the game comes
not from declarative representation, but is derived by interrogation of a procedural …

Cognitive radio is viewed as a novel approach for improving the utilization of a precious
natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a …

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 work studies an algorithm, which we call magnetic mirror descent, that is inspired by
mirror descent and the non-Euclidean proximal gradient algorithm. Our contribution is …

No-press Diplomacy is a complex strategy game involving both cooperation and competition
that has served as a benchmark for multi-agent AI research. While self-play reinforcement …

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) …