The recently proposed option-critic architecture (Bacon, Harb, and Precup 2017) provides a
stochastic policy gradient approach to hierarchical reinforcement learning. Specifically, it …

Despite the advancement of machine learning techniques in recent years, state-of-the-art
systems lack robustness to “real world” events, where the input distributions and tasks …

The basic assumption of teacher-education is that an effective teacher must have the mastery
of subject content as well as he should have the fun understanding of theoretical and …

Triple cation mixed halide perovskite solar cells have exhibited a great deal of interest and
could be the potential candidate with an enhanced power conversion efficiency (PCE) as …

Deep neural networks can approximate functions on different types of data, from images to
graphs, with varied underlying structure. This underlying structure can be viewed as the …

Today is the world of moves with faster speed and reduced time. With the increase in population,
there has been a sudden increase in the amount of vehicular traffic. These increased …

This research paper introduces a novel symmetric key encryption algorithm based on the
Extended MSA (Modified Substitution Algorithm) method. The proposed algorithm aims to …

We study orbital Mott transition in two dimensional pyrochlore lattice, using a two orbital
Hubbard model with only inter-orbital electronic hopping. We use a real space Monte Carlo …

In today's technology-driven era, the imperative for predictive maintenance and advanced
diagnostics extends beyond aviation to encompass the identification of damages, failures, and …

We propose a novel parameterized skill-learning algorithm that aims to learn transferable
parameterized skills and synthesize them into a new action space that supports efficient …