To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

Continual learning is a sub-field of machine learning, which aims to allow machine learning
models to continuously learn on new data, by accumulating knowledge without forgetting …

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

Continual pre-training has been urgent for adapting a pre-trained model to a multitude of
domains and tasks in the fast-evolving world. In practice, a continually pre-trained model is …

Reinforcement Learning (RL) has been successful in various domains like robotics, game
playing, and simulation. While RL agents have shown impressive capabilities in their …

How to train a generalizable meta-policy by continually learning a sequence of tasks? It is a
natural human skill yet challenging to achieve by current reinforcement learning: the agent is …

For safe motion planning in real-world autonomous vehicles require behavior prediction
models that are reliable and robust to distribution shifts. The recent studies suggest that the …

The capability of continuously learning new skills via a sequence of pre-collected offline
datasets is desired for an agent. However, consecutively learning a sequence of offline tasks …

Lifelong learning offers a promising paradigm of building a generalist agent that learns and
adapts over its lifespan. Unlike traditional lifelong learning problems in image and text …

Deep reinforcement learning has shown promising application effects in many fields.
However, issues such as low sample efficiency and weak knowledge transfer and …