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 …

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 …

Abstract Knowledge distillation is an effective approach to learn compact models (students)
with the supervision of large and strong models (teachers). As empirically there exists a …

Deep reinforcement learning (RL) has shown immense potential for learning to control
systems through data alone. However, one challenge deep RL faces is that the full state of …

Many of the challenges facing today's reinforcement learning (RL) algorithms, such as
robustness, generalization, transfer, and computational efficiency are closely related to …

Embodied AI models often employ off the shelf vision backbones like CLIP to encode their
visual observations. Although such general purpose representations encode rich syntactic …

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 …

The rapidly growing encrypted traffic hides a large number of malicious behaviours. The
difficulty of collecting and labelling encrypted traffic makes the class distribution of dataset …

The problem of autonomous navigation has attracted significant attention from robotics
research community in the last few decades. In this paper, we address the problem of low …

It is of significance for an agent to autonomously explore the environment and learn a widely
applicable and general-purpose goal-conditioned policy that can achieve diverse goals …