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 …

The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to
produce RL algorithms whose policies generalise well to novel unseen situations at …

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 …

In recent years, deep reinforcement learning (DRL) has garnered substantial attention in the
context of enhancing resilience in power and energy systems. Resilience, characterized by …

The ability to continuously acquire new knowledge and skills is crucial for autonomous
agents. Existing methods are typically based on either fixed-size models that struggle to …

Lifelong learning aims to create AI systems that continuously and incrementally learn during
a lifetime, similar to biological learning. Attempts so far have met problems, including …

The advancement of continual reinforcement learning (RL) has been facing various
obstacles, including standardized metrics and evaluation protocols, demanding …

The modern paradigm in machine learning involves pre-training on diverse data, followed
by task-specific fine-tuning. In reinforcement learning (RL), this translates to learning via …

Continual learning is one of the key components of human learning and a necessary
requirement of artificial intelligence. As dialogue can potentially span infinitely many topics …

Task-orienteddialogue systems aid users in achieving their goals for specific tasks, eg,
booking a hotel room or managing a schedule. The systems experience various changes …