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 …

Most existing benchmarks for grounding language in interactive environments either lack
realistic linguistic elements, or prove difficult to scale up due to substantial human …

Reinforcement learning (RL) algorithms face two distinct challenges: learning effective
representations of past and present observations, and determining how actions influence …

World models are a fundamental component in model-based reinforcement learning
(MBRL). To perform temporally extended and consistent simulations of the future in partially …

Artificial Intelligence of Things (AIoT) is an emerging frontier based on the deep fusion of
Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The fundamental goal of …

In popular media, there is often a connection drawn between the advent of awareness in
artificial agents and those same agents simultaneously achieving human or superhuman …

Effective decision making involves flexibly relating past experiences and relevant contextual
information to a novel situation. In deep reinforcement learning (RL), the dominant paradigm …

Memory Gym is a novel benchmark for challenging Deep Reinforcement Learning agents to
memorize events across long sequences, be robust to noise, and generalize. It consists of …

Humans perceive discrete events such as “restaurant visits” and “train rides” in their
continuous experience. One important prerequisite for studying human event perception is …

Object packing by autonomous robots is an important challenge in warehouses and logistics
industry. Most conventional data-driven packing planning approaches focus on regular …