Contemporary theories of reinforcement learning are rooted in the dopaminergic reward system.
当代的强化学习理论是基于多巴胺奖赏系统。
Due to the theoretical limitation that it assumes that an environment is Markovian, traditional reinforcement learning algorithms cannot be applied directly to multi-agent system.
由于强化学习理论的限制，在多智能体系统中马尔科夫过程模型不再适用，因此不能把强化学习直接用于多智能体的协作学习问题。
This characteristic of reinforcement learning must increase learning difficulty for intelligent system and learning time also grows up.
强化学习的这种特性必然增加智能系统的困难性，学习时间增长。
For vector control AC drive system, the thesis presented a fuzzy neural network speed controller based on reinforcement learning.
针对矢量控制交流调速系统，该文提出并设计了一种基于再励学习的模糊神经网络速度控制器。
MAXQ, a hierarchical reinforcement learning method for multi-agent system, is proposed in recent years.
MAXQ分层多智能体学习方法是近年来被提出的一种新方法。
MAXQ, a hierarchical reinforcement learning method for multi-agent system, is proposed in recent years.
MAXQ分层多智能体学习方法是近年来被提出的一种新方法。
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