Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of
indispensable factors to achieve human-level or super-human AI systems. On the other …

With the widespread adoption of deep learning, reinforcement learning (RL) has
experienced a dramatic increase in popularity, scaling to previously intractable problems …

Developing a general algorithm that learns to solve tasks across a wide range of
applications has been a fundamental challenge in artificial intelligence. Although current …

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 …

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 …

We investigated whether deep reinforcement learning (deep RL) is able to synthesize
sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be …

Deep reinforcement learning has recently seen huge success across multiple areas in the
robotics domain. Owing to the limitations of gathering real-world data, ie, sample inefficiency …

We propose a simple data augmentation technique that can be applied to standard model-
free reinforcement learning algorithms, enabling robust learning directly from pixels without …

Reinforcement learning (RL) provides an appealing formalism for learning control policies
from experience. However, the classic active formulation of RL necessitates a lengthy active …

In reinforcement learning (RL) research, it is common to assume access to direct online
interactions with the environment. However in many real-world applications, access to the …