部分可观测领域中不确定性最大化的认知研究
Uncertainty Maximization in Partially Observable Domains: A Cognitive Perspective
摘要：
面对其应用领域的日益复杂性，人工学习代理现在能够扩大其处理来自其与环境交互的大量信息的能力。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，这对学习过程本身并不一定有益。这项工作利用了部分可观察域上定义的学习系统的属性，通过选择性地关注更有可能表达环境过渡状态之间因果相互作用的特定类型的信息。基于时间差位移准则的观测空间的自适应掩蔽使得定义在部分可观测马尔可夫过程上的时间差算法的收敛性得到显著改善。
英文摘要：
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 temporal difference displacement criterion enabled a significant improvement in convergence of temporal difference algorithms defined over a partially observable Markov process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
