一、Independent Learning Algorithms In this category, each agent is trained independently, ignoring the presence of other agents in the environment. In this category, we have three algorithms: Independent Q-Lea
看完MAPPO之后我也在想,是不是也有TRPO的多智能体版本,果不其然,前几天就搜到了这篇论文Trust Region Methods in Multi-Agent Reinforcement Learning,感觉是TRPO的多智能体版本,但也不全是,这篇文章在智能体策略更新顺序以及更新目标上提出了新的方案。本人虽然有一定的TRPO理论基础,但看起来还是挺吃力的,不过...
multi-agent reinforcement learning中文-概述说明以及解释 1.引言 1.1概述 多智能体强化学习是一种重要的机器学习方法,它能够让多个智能体在相互交互的环境中学习并协同解决问题。在传统的强化学习中,只有一个智能体与环境进行交互,而多智能体强化学习则引入了多个智能体之间的相互作用。通过学习如何与其他智能体进行...
我们将多场景的排序问题看成一个完全合作的、部分可观测的多智能体序列决策问题,利用Multi-Agent Reinforcement Learning的方法来尝试着对问题进行建模。该模型以各个场景为Agent,让各个场景不同的排序策略共享同一个目标,同时在一个场景的排序结果会考虑该用户在其他场景的行为和反馈。也就是,不同场景不同的action,但...
的Q值,这些期望的q值可以用于agent的动作选择,以及Q-learning的更新,就像在标准的单智能体的Q-learning算法中一样。 (2)假设其他智能体将根据某种策略进行博弈 例如:在minimax Q-learning算法(Littman, 1994)中,该算法是针对二主体零和问题而开发的,学习主体假设其对手将采取使学习者收益最小化的行动。这意味着单...
We chose to address the challenge of StarCraft using general-purpose learning methods that are in principle applicable to other complex domains: a multi-agent reinforcement learning algorithm that uses data from both human and agent games within a diverse league of continually adapting strategies and ...
In reinforcement learning, complicated applications require involving multiple agents to handle different kinds of tasks simultaneously. However, increasing the number of agents brings in the challenges on managing the interactions among them. In this chapter, according to the optimization problem for each...
a reinforcement learning algorithm for agent-based 热度: Markovgamesasaframeworkformulti-agentreinforcementlearning MichaelL.Littman BrownUniversity/Bellcore DepartmentofComputerScience BrownUniversity Providence,RI02912-1910 mlittman@cs.brown.edu Abstract ...
A recommendation algorithm using multi-level association rules Human-level control through deep reinforcement learning.pdf A reinforcement learning system embedded agent with neural network based adaptive hierarchical memory structure 改进单智能体和多智能体深度强化学习方法 Improving single and multi-agent...
文章链接:Multi-Agent Reinforcement Learning is a Sequence Modeling Problem 文章总结 背景 这篇文章着眼于多智能体强化学习(MARL)的问题,希望通过引入序列模型(SM)来解决这一领域的挑战。 创新点 提出了一种新的解决协作MARL问题的通用框架,将其统一为类似Transformer的编码器-解码器模型。 利用多智能体优势分解定理...