Game theory studies the mathematical models for self-interested individuals. Nash
equilibrium is arguably the most central solution in game theory. While finding the Nash …

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 …

Many real-world applications can be described as large-scale games of imperfect
information, which require extensive prior domain knowledge, especially in competitive or …

Promoting behavioural diversity is of critical importance in multi-agent reinforcement
learning, since it helps the agent population maintain robust performance when …

Self-play, characterized by agents' interactions with copies or past versions of itself, has
recently gained prominence in reinforcement learning. This paper first clarifies the …

Few multi-agent reinforcement learning (MARL) researches on Google research football
(GRF) focus on the 11-vs-11 multi-agent full-game scenario and to the best of our …

In recent years, with the rapid development of swarm control technology, the swarm system
has shown broad application prospects in military, civil, and other fields, which has gradually …

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 …

The resolution of extensive-form zero-sum games is a fundamental challenge in
computational game theory, addressed through various algorithms, each with unique …

Deep reinforcement learning has demonstrated remarkable performance in autonomous
vehicle control. However, the increasing threat of cyber-physical attacks, which can alter …