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 …

While reinforcement learning (RL) achieves tremendous success in sequential decision-
making problems of many domains, it still faces key challenges of data inefficiency and the …

Recently in the field of unsupervised representation learning, strong identifiability results for
disentanglement of causally-related latent variables have been established by exploiting …

Dealing with non-stationarity in environments (eg, in the transition dynamics) and objectives
(eg, in the reward functions) is a challenging problem that is crucial in real-world …

In many reinforcement learning tasks, the agent has to learn to interact with many objects of
different types and generalize to unseen combinations and numbers of objects. Often a task …

In unsupervised causal representation learning for sequential data with time-delayed latent
causal influences, strong identifiability results for the disentanglement of causally-related …

Offline meta-reinforcement learning (OMRL) utilizes pre-collected offline datasets to
enhance the agent's generalization ability on unseen tasks. However, the context shift …

Reinforcement learning is an essential paradigm for solving sequential decision problems
under uncertainty. Despite many remarkable achievements in recent decades, applying …

Deep reinforcement learning (DRL) has led to a wide range of advances in sequential
decision-making tasks. However, the complexity of neural network policies makes it difficult …