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 …

Visual reinforcement learning (RL), which makes decisions directly from high-dimensional
visual inputs, has demonstrated significant potential in various domains. However …

Reinforcement learning methods trained on few environments rarely learn policies that
generalize to unseen environments. To improve generalization, we incorporate the inherent …

While agents trained by Reinforcement Learning (RL) can solve increasingly challenging
tasks directly from visual observations, generalizing learned skills to novel environments …

Generalization is a central challenge for the deployment of reinforcement learning (RL)
systems in the real world. In this paper, we show that the sequential structure of the RL …

The ability to autonomously learn behaviors via direct interactions in uninstrumented
environments can lead to generalist robots capable of enhancing productivity or providing …

Abstract Reconstruction-based Model-Based Reinforcement Learning (MBRL) agents, such
as Dreamer, often fail to discard task-irrelevant visual distractions that are prevalent in …

Abstract Visual Reinforcement Learning (Visual RL), coupled with high-dimensional
observations, has consistently confronted the long-standing challenge of out-of-distribution …

Current model-based reinforcement learning methods struggle when operating from
complex visual scenes due to their inability to prioritize task-relevant features. To mitigate …

Visual model-based RL methods typically encode image observations into low-dimensional
representations in a manner that does not eliminate redundant information. This leaves them …