A critical review of the existing Internal Combustion Engine (ICE) modeling, optimization,
diagnosis, and control challenges and the promising state-of-the-art Machine Learning (ML) …

Abstract Path Planning problems with Unmanned Aerial Vehicles (UAVs) are among the
most studied knowledge areas in the related literature. However, few of them have been …

Offline reinforcement learning (RL), also known as batch RL, offers the prospect of policy
optimization from large pre-recorded datasets without online environment interaction. It …

In recent years some researchers have explored the use of reinforcement learning (RL)
algorithms as key components in the solution of various natural language processing (NLP) …

Testing in Continuous Integration (CI) involves test case prioritization, selection, and
execution at each cycle. Selecting the most promising test cases to detect bugs is hard if …

Reinforcement learning offers a promising methodology for developing skills for simulated
characters, but typically requires working with sparse hand-crafted features. Building on …

This monograph presents a new framework for learning-based control synthesis of
continuous-time dynamical systems with unknown dynamics. The new design paradigm …

We propose a novel policy update that combines regularized policy optimization with model
learning as an auxiliary loss. The update (henceforth Muesli) matches MuZero's state-of-the …

The energy-efficient train operation methodology is the focus of this paper, and a Q-Learning-
based eco-driving approach is proposed. Firstly, the core idea of energy-distribution-based …

Many traditional reinforcement-learning algorithms have been designed for problems with
small finite state and action spaces. Learning in such discrete problems can been difficult …