The LSTM network was proposed to overcome the difficulty in learning long-term
dependence, and has made significant advancements in applications. With its success and …

In many partially observable scenarios, Reinforcement Learning (RL) agents must rely on
long-term memory in order to learn an optimal policy. We demonstrate that using techniques …

In this article, we consider a subclass of partially observable Markov decision process
(POMDP) problems which we termed confounding POMDPs. In these types of POMDPs …

Modeling an unknown dynamical system is crucial in order to predict the future behavior of
the system. A standard approach is training recurrent models on measurement data. While …

In recent years, there has been a rise in complex and computationally expensive machine
learning systems with many hyperparameters, such as deep convolutional neural networks …