Abstract
Deep neural networks have become commonplace in the domain of reinforcement learning, but are often expensive in terms of the number of parameters needed. While compressing deep neural networks has of late assumed great importance to overcome this drawback, little work has been done to address this problem in the context of reinforcement learning agents. This work aims at making first steps towards model compression in an RL agent. In particular, we compress networks to drastically reduce the number of parameters in them (to sizes less than 3% of their original size), further facilitated by applying a global max pool after the final convolution layer, and propose using Actor-Mimic in the context of compression. Finally, we show that this global max-pool allows for weakly supervised object localization, improving the ability to identify the agent's points of focus.
Abstract (translated)
在强化学习领域，深层神经网络已经变得司空见惯，但在所需参数的数量上往往很昂贵。虽然压缩深层神经网络最近被认为是克服这一缺点的重要手段，但在强化学习主体的背景下，几乎没有做过任何工作来解决这一问题。这项工作的目的是在一个RL代理中朝着模型压缩迈出第一步。特别是，我们压缩网络以大幅减少其中的参数数量（大小小于原始大小的3%），在最终卷积层后应用全局最大池进一步简化，并建议在压缩上下文中使用actor mimic。最后，我们证明这个全局最大值池允许弱监控对象定位，提高了识别代理焦点的能力。
URL
https://arxiv.org/abs/1904.09489
