Offline reinforcement learning has shown great promise in leveraging large pre-collected
datasets for policy learning, allowing agents to forgo often-expensive online data collection …

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 …

Multi-task learning (MTL) is a subfield of machine learning in which multiple tasks are
simultaneously learned by a shared model. Such approaches offer advantages like …

We present BRIEE, an algorithm for efficient reinforcement learning in Markov Decision
Processes with block-structured dynamics (ie, Block MDPs), where rich observations are …

One practical challenge in reinforcement learning (RL) is how to make quick adaptations
when faced with new environments. In this paper, we propose a principled framework for …

In this article, we propose a communicative learning (CL) formalism that unifies existing
machine learning paradigms, such as passive learning, active learning, algorithmic …

While reinforcement learning has achieved remarkable successes in several domains, its
real-world application is limited due to many methods failing to generalise to unfamiliar …

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

While Reinforcement Learning (RL) has made great strides towards solving increasingly
complicated problems, many algorithms are still brittle to even slight environmental changes …

In reinforcement learning (RL), when defining a Markov Decision Process (MDP), the
environment dynamics are implicitly assumed to be stationary. This assumption of …