Explainable artificial intelligence (XAI) has received great interest in the recent decade, due
to its importance in critical application domains, such as self-driving cars, law, and …

In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

In order to improve reproducibility, deep reinforcement learning (RL) has been adopting
better scientific practices such as standardized evaluation metrics and reporting. However …

Tremendous progress has been made in reinforcement learning (RL) over the past decade.
Most of these advancements came through the continual development of new algorithms …

Modern machine learning requires system designers to specify aspects of the learning
pipeline, such as losses, architectures, and optimizers. Meta-learning, or learning-to-learn …

Offline preference optimization is a key method for enhancing and controlling the quality of
Large Language Model (LLM) outputs. Typically, preference optimization is approached as …

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …

Intelligent agents should have the ability to leverage knowledge from previously learned
tasks in order to learn new ones quickly and efficiently. Meta-learning approaches have …

Abstract The 5th Generation (5G) and beyond networks are expected to offer huge
throughputs, connect large number of devices, support low latency and large numbers of …