Radio access network (RAN) technologies continue to evolve, with Open RAN gaining the
most recent momentum. In the O-RAN specifications, the RAN intelligent controllers (RICs) …

Many Deep Reinforcement Learning (D-RL) algorithms rely on simple forms of exploration
such as the additive action noise often used in continuous control domains. Typically, the …

Decentralization is a central characteristic of biological motor control that allows for fast
responses relying on local sensory information. In contrast, the current trend of Deep …

Natural intelligence processes experience as a continuous stream, sensing, acting, and
learning moment-by-moment in real time. Streaming learning, the modus operandi of classic …

Modern deep policy gradient methods achieve effective performance on simulated robotic
tasks, but they all require large replay buffers or expensive batch updates, or both, making …

In this paper, we address the problem of vision-based obstacle avoidance for robotic
manipulators. This topic poses challenges for both perception and motion generation. While …

Portfolio Management is the process of overseeing a group of investments, referred to as a
portfolio, with the objective of achieving predetermined investment goals and objectives …

We explore the reinforcement learning approach to designing controllers by extensively
discussing the case of a quadcopter attitude controller. We provide all details allowing to …

Robot learning is a powerful paradigm for skill acquisition in robotics. Learning such skills
based on data can be very expensive in terms of interactions with the environment …

Telemedicine applications have recently received substantial potential and interest,
especially after the COVID-19 pandemic. Remote experience will help people get their …