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Update Chapter 10 #86
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YanjieGao
reviewed
Apr 21, 2022
YanjieGao
reviewed
Apr 21, 2022
- 对于采样器:策略(Policy, 即Q-Network)与环境交互,同样涉及探索与利用。但是GORILA里定义了一个Bundled Mode,即采样器的策略与学习器中实时更新的Q-Network是捆绑的。 | ||
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- 对于学习器: 学习器中对于Q-Network的参数梯度会发给参数服务器。 | ||
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- 对于重放缓冲区:在GORILA里分两种形式,在本地(local) 模式下就存在采样器所在的机器上;而多机(Golbal)模式下将所有的数据聚合在分布式数据库中,这样的优点是可伸缩性好,缺点是会有额外的通信开销。 | ||
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- 对于参数服务器:存储Q-Network中参数的梯度(Gradient)的变化,好处是可以让Q-Network进行回滚,并且可以通过多个梯度(Gradient)来使训练过程更加稳定。在分布式环境中,不可避免的就是稳定性问题(比如节点消失、网速变慢或机器变慢)。GORILA中采用了几个策略来解决这个问题,如丢弃过旧的和损失值(Loss)太过偏离均值时的梯度。 | ||
>>>>>>> main |
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remove
YanjieGao
reviewed
Apr 21, 2022
图中可能时间作为下横轴,圆圈内为算法名更好些 |
Closed
YanjieGao
approved these changes
Apr 21, 2022
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