《Playing Atari with Deep Reinforcement Learning 》论文阅读笔记和分析（DQN 2013版）

其他 2018-08-05 06:47:11 阅读次数: 0

DL难以应用于RL的原因

标签：DL需要大量标签好的训练集，而RL在一个具有延迟性、噪声、稀疏性的标量reward signal中学习。这种延迟存在于action 和其reward之中，使得难以建立出类似监督学习中输入与目标的直接关联
相关性：DL中的样本数据之间是不互相影响的，而RL 的state序列是高度相关性的（因此导致其样本也是高度相关性的）。
概率分布：DL中的数据分布概率是固定的，而RL中的数据分布概率随着学习的策略而改变。

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转载自blog.csdn.net/linyijiong/article/details/81269749

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