Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision
making by using interaction samples of an agent with its environment and the potentially …

Inspired by progress in large-scale language modeling, we apply a similar approach
towards building a single generalist agent beyond the realm of text outputs. The agent …

Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is
based on the social value of Generation Z that online and offline selves are not different …

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 …

Foundation models pretrained on diverse data at scale have demonstrated extraordinary
capabilities in a wide range of vision and language tasks. When such models are deployed …

Intelligent agents need to generalize from past experience to achieve goals in complex
environments. World models facilitate such generalization and allow learning behaviors …

Offline reinforcement learning (RL) refers to the problem of learning policies entirely from a
batch of previously collected data. This problem setting is compelling, because it offers the …

Designing effective model-based reinforcement learning algorithms is difficult because the
ease of data generation must be weighed against the bias of model-generated data. In this …

Reinforcement learning allows solving complex tasks, however, the learning tends to be task-
specific and the sample efficiency remains a challenge. We present Plan2Explore, a self …