论文标题
自主对控制功能的学习：具有体现和位置剂的实验
Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents
论文作者
论文摘要
如先前的研究中所述，可以通过包括专门用于通过自我监督方法训练的特征提取的神经模块来增强进化或增强学习算法对连续控制优化的功效。在本文中，我们报告了支持这一假设的其他实验，并证明了特征提取所提供的优势不限于从降低降低性降低或涉及基于同类中心知觉运行的药物的问题。我们介绍了一种允许在策略网络培训期间继续训练特征拔除模块的方法，并提高功能提取的功效。最后，我们比较替代特征提取方法，并表明序列到序列学习的结果比以前研究中考虑的方法更好。
As discussed in previous studies, the efficacy of evolutionary or reinforcement learning algorithms for continuous control optimization can be enhanced by including a neural module dedicated to feature extraction trained through self-supervised methods. In this paper we report additional experiments supporting this hypothesis and we demonstrate how the advantage provided by feature extraction is not limited to problems that benefit from dimensionality reduction or that involve agents operating on the basis of allocentric perception. We introduce a method that permits to continue the training of the feature-extraction module during the training of the policy network and that increases the efficacy of feature extraction. Finally, we compare alternative feature-extracting methods and we show that sequence-to-sequence learning yields better results than the methods considered in previous studies.
