It is illustrated and compared to other reinforcement learning algorithms.

仿真研究将该方法与其他再励学习方法进行了比较。

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The thesis mainly focuses on the dynamic scheduling method based on the averaged rewards reinforcement learning algorithms.

论文主要研究了基于平均型强化学习算法的动态调度方法。

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It is rational to adopt the average reward reinforcement learning algorithms for solving the absorbing goal states cyclical tasks.

对于有吸收目标状态的循环任务，比较合理的方法是采用基于平均报酬模型的强化学习。

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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。

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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。

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