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.

中文翻译：

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控制特征的自主学习：具体化和定位代理的实验

正如之前的研究中所讨论的那样，通过包含一个专用于通过自监督方法训练的特征提取的神经模块，可以增强进化或强化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，并展示了特征提取提供的优势如何不仅限于受益于降维或涉及基于异中心感知操作的代理的问题。我们引入了一种方法，允许在策略网络训练期间继续训练特征提取模块，并提高特征提取的效率。最后，