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。
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。
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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