Foerster等人2016年在NIPS上提出Reinforced Inter-Agent Learning(RIAL)和Differentiable Inter-Agent Learning[4]是两种使用深度网络学习通信的算法(PS:Foerster写了很多DRML的文章,被很多其他重要文献引用)。都使用神经网络来输出智能体的Q值以及需要传给其他智能体的消息。RIAL是基于深度循环Q网络(DRQN[5])并且使用参数共...
在正常形式的博弈(normal-form game)中,NE表示一个联合策略的平衡点,其中每个agent根据相对于其他agent的最佳反应(best response)来行动。最佳反应通过考虑其他所有agent的策略来获得最佳汇报。由于最佳反应取决于与其他agent的相对奖励,agent所获得的绝对奖励是不重要的,换句话说,对所有玩家的奖励进行正的仿射变换是不改...
13. 多智能体强化学习(1_2):基本概念++Multi-Agent+Reinforcement+Learning 18:37 14. 多智能体强化学习(2_2):三种架构++Multi-Agent+Reinforcement+Learning 18:37 15. 策略梯度中的Baseline+(1_4) 09:48 16. REINFORCE+with+Baseline+(策略梯度中的Baseline+2_4) 11:26 17. A2C+方法+(策略梯度...
14. 多智能体强化学习(2_2):三种架构++Multi-Agent+Reinforcement+Learning是杀疯了!首次使用【强化学习】训练AI玩王者荣耀,真是让人叹为观止,建议收藏!解放双手!——(人工智能、深度学习、神经网络、机器学习、机器学习算法)的第13集视频,该合集共计20集,视频收
De Schutter, "Multi-agent reinforce- ment learning: An overview," in Innovations in multi-agent systems and applications-1. Springer, 2010, pp. 183-221.Buşoniu, L., Babuška, R., Schutter, B. Multi-Agent Reinforcement Learning: An Overview. In: Srinivasan, D., Jain, L. eds. (...
与 single-agent 系统不同的是,multi-agent 的系统,每一个智能体的选择不仅依赖于 local environment state,而且受到 context information 的影响。所以,我们设计了一个 context-aware module,来维持一个 joint internal state of agents,用一个 RNN 网络将 history context information 进行总结。为了能够使之更加...
Ultimately, in order to verify the effectiveness and validity of the proposed method, and also for verification of learning efficiency, it is compared with conventional method and showed promising results. 展开 关键词:multiagent reinforcenment learning soccer ...
Planning methods have been developed forMacDec-POMDPs which have been demonstrated in realistic robotics problems [8, 9, 10, 11], butonly limited learning settings have been considered [12].Nevertheless, a principled way is still missing to generalize the above multi-agent deep reinforce-ment ...
Minimizing computational costs for single-task problem:包括REINFORCE,Q Learning和actor-critic methods。本文更强调MTL,因此采用了multi-agent reinforcement learning训练算法和一个递归决策过程。 准确地说,我们的工作是automated architecture search。文本是第一篇MTL+hard routing decision。
and Kobayashi, S.: Rationality of Re- ward Sharing in Multi-agent Reinforcement Learn- ing, Second Paci c Rim International Workshop on Multi-Agents, pp.111-125, (1999).K. Miyazaki and S. Kobayashi, Rationality of Reward Sharing in Multi-agent Reinforce- ment Learning, Proc. of the ...