Rapid advancement of machine learning makes it possible to consider large amounts of data to learn from. Learning agents may get data ranging on real intervals directly from the environment they interact with, in a process usually time expensive. To improve learning and manage these data, approximated models and memory mechanisms are adopted. In most of the implementations of reinforcement learning facing this type of data, approximation is obtained by neural networks and the process of drawing information from data is mediated by a short-term memory that stores the previous experiences for additional relearning, to speed-up the learning process, mimicking what is done by people. In this paper, we are proposing a novel computational approach able to selectively filter the information, or cognitive load, for the agent’s short-term memory, thus emulating the attention mechanism characteristic of human perception. In this work, we use genetic algorithms in order to evolve the most efficient attention filter mechanism that would be able to provide the agent with an optimal perception for a specific environment by discriminating which experiences are valuable for the learning process. This approach can evolve a filter which can able to provide an optimal cognitive load of the experiences entering in the agent’s short-term memory of a limited capacity. The evolved sampling dynamics can also lead to the emergence of intrinsically motivated curiosity.

中文翻译：

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选择性感知作为使代理适应环境的机制：一种进化方法

机器学习的快速发展使得考虑大量要学习的数据成为可能。学习代理可以直接从它们与之交互的环境中获取真实间隔范围内的数据，这个过程通常很耗时。为了改进学习和管理这些数据，采用了近似模型和记忆机制。在大多数面向此类数据的强化学习实现中，近似值是通过神经网络获得的，从数据中提取信息的过程由短期记忆介导，该记忆存储先前的经验以进行额外的重新学习，以加快学习过程，模仿人们所做的事情。在本文中，我们提出了一种新颖的计算方法，能够选择性地过滤代理短期记忆的信息或认知负荷，从而模拟人类感知的注意力机制特征。在这项工作中，我们使用遗传算法来发展最有效的注意力过滤机制，该机制能够通过区分哪些经验对学习过程有价值，为代理提供对特定环境的最佳感知。这种方法可以进化出一个过滤器，该过滤器能够为进入代理的有限容量的短期记忆中的体验提供最佳认知负荷。进化的采样动态也可以导致内在动机的好奇心的出现。我们使用遗传算法来发展最有效的注意力过滤机制，通过区分哪些经验对学习过程有价值，能够为代理提供对特定环境的最佳感知。这种方法可以进化出一个过滤器，该过滤器能够为进入代理的有限容量的短期记忆中的体验提供最佳认知负荷。进化的采样动态也可以导致内在动机的好奇心的出现。我们使用遗传算法来发展最有效的注意力过滤机制，通过区分哪些经验对学习过程有价值，能够为代理提供对特定环境的最佳感知。这种方法可以进化出一个过滤器，该过滤器能够为进入代理的有限容量的短期记忆中的体验提供最佳认知负荷。进化的采样动态也可以导致内在动机的好奇心的出现。