Deep reinforcement learning has proved to be a fruitful method in various tasks in the field of
artificial intelligence during the last several years. Recent works have focused on deep …

Much research in artificial intelligence is concerned with the development of autonomous
agents that can interact effectively with other agents. An important aspect of such agents is …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with
other agents through autonomous exploration of the environment. However, learning a …

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 …

Seamlessly interacting with humans or robots is hard because these agents are non-
stationary. They update their policy in response to the ego agent's behavior, and the ego …

Opponent modeling is the ability to use prior knowledge and observations in order to predict
the behavior of an opponent. This survey presents a comprehensive overview of existing …

多Agent深度强化学习综述 E-mail Alert RSS 2.765 2022影响因子 (CJCR) 中文核心 EI 中国科技
核心 Scopus CSCD 英国科学文摘 首页 期刊介绍 1.基本信息 2.收录与获奖 3.近年指标 期刊在线 …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

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 …