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 …

Forgetting refers to the loss or deterioration of previously acquired knowledge. While
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …

Pretrained language models (PLMs) are today the primary model for natural language
processing. Despite their impressive downstream performance, it can be difficult to apply …

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …

Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …

The human brain rewires itself for neuroplasticity in the presence of new tasks. We are
inspired to harness this key process in continual reinforcement learning, prioritizing …

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …

General intelligence requires quick adaption across tasks. While existing reinforcement
learning (RL) methods have made progress in generalization, they typically assume only …

In open multi-agent environments, the agents may encounter unexpected teammates.
Classical multi-agent learning approaches train agents that can only coordinate with seen …

Lifelong deep reinforcement learning (DRL) approaches are commonly employed to adapt
continuously to new tasks without forgetting previously acquired knowledge. While current …