Faced with an ever-increasing complexity of their domains of application, artificial learning agents are now able to scale up in their ability to process an overwhelming amount of information coming from their interaction with an environment. However, this process of scaling does come with a cost of encoding and processing an increasing amount of redundant information that is not necessarily beneficial to the learning process itself. This work exploits the properties of the learning systems defined over partially observable domains by selectively focusing on the specific type of information that is more likely to express the causal interaction among the transitioning states of the environment. Adaptive masking of the observation space based on the \textit{temporal difference displacement} criterion enabled a significant improvement in convergence of temporal difference algorithms defined over a partially observable Markov process.

中文翻译：

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部分可观察域中的不确定性最大化：认知角度

面对其应用领域的日益复杂性，人工学习代理现在能够扩展其处理与环境交互作用产生的大量信息的能力。然而，这种缩放过程确实伴随着编码和处理越来越多的冗余信息的成本，这不一定对学习过程本身有利。这项工作通过选择性地关注特定类型的信息，从而更容易地表达环境的过渡状态之间的因果关系，从而利用了在部分可观察的域中定义的学习系统的属性。