The growth in the volume of data generated, consumed, and stored, which is estimated to
exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for …

Reinforcement Learning is one of the many machine learning paradigms. With no labelled
data, it is concerned with balancing the exploration and exploitation of an environment with …

One major cost of improving the automotive fuel economy while simultaneously reducing
tailpipe emissions is increased powertrain complexity. This complexity has consequently …

This demo paper presents a novel approach to inducing fear in a horror game inspired by
the SCP-087 creepypasta. The game features an infinite staircase that players must …

Reinforcement learning (RL) methods work in discrete time. In order to apply RL to
inherently continuous problems like robotic control, a specific time discretization needs to be …

Games have always been a popular form of entertainment and with the advancements in
technology, the integration of Artificial Intelligence (AI) in gaming has revolutionized the …

Deep Reinforcement Learning (DRL) has emerged as a transformative paradigm with
profound implications for gaming, robotics, real-world control systems, and beyond. This …

The push for improvements in fuel economy while reducing tailpipe emissions has resulted
in significant increases in automotive powertrain complexity, subsequently increasing the …

Integrating deep learning into the Scrabble board game introduces a level of intricacy and
sophistication to the overall gaming experience. This study delves into the application of …

Deep Reinforcement Learning (DRL) has emerged as a transformative paradigm with
profound implications for gaming, robotics, real-world control systems, and beyond. This …