The key challenge in multiagent learning is learning a best response to the behaviour of
other agents, which may be non-stationary: if the other agents adapt their strategy as well …

对手建模作为多智能体博弈对抗的关键技术, 是一种典型的智能体认知行为建模方法.
介绍了多智能体博弈对抗几类典型模型, 非平稳问题和元博弈相关理论; 梳理总结对手建模方法 …

Interactions in multiagent systems are generally more complicated than single agent ones.
Game theory provides solutions on how to act in multiagent scenarios; however, it assumes …

As the key technology of multi-agent game confrontation, opponent modeling is a typical
cognitive modeling method of agent's behavior. Several typical models of multi-agent game …

One challenging problem in multiagent systems is to cooperate or compete with non-
stationary agents that change behavior from time to time. An agent in such a non-stationary …

Multi-agent algorithms aim to find the best response in strategic interactions. While many
state-of-the-art algorithms assume repeated interaction with a fixed set of opponents (or …

In many situations, agents are required to use a set of strategies (behaviors) and switch
among them during the course of an interaction. This work focuses on the problem of …

Discounted repeated games are currently being used to model the conflicts that arise
between the nodes in a wireless network, such as distributed resource allocation …

The success or failure of any learning algorithm is partially due to the exploration strategy it
exerts. However, most exploration strategies assume that the environment is stationary and …

Learning adversarial policy aims to learn behavioural strategies for agents with different
goals, is one of the most significant tasks in multi-agent systems. Multi-agent reinforcement …