We introduce a value-based RL agent, which we call BBF, that achieves super-human
performance in the Atari 100K benchmark. BBF relies on scaling the neural networks used …

Autonomous driving promises transformative improvements to transportation, but building
systems capable of safely navigating the unstructured complexity of real-world scenarios …

Algorithmic reasoning requires capabilities which are most naturally understood through
recurrent models of computation, like the Turing machine. However, Transformer models …

Visual model-based reinforcement learning (RL) has the potential to enable sample-efficient
robot learning from visual observations. Yet the current approaches typically train a single …

Data-driven model predictive control has two key advantages over model-free methods: a
potential for improved sample efficiency through model learning, and better performance as …

Transformers, originally devised for natural language processing (NLP), have also produced
significant successes in computer vision (CV). Due to their strong expression power …

In this big data era, the use of large dataset in conjunction with machine learning (ML) has
been increasingly popular in both industry and academia. In recent times, the field of …

Deep reinforcement learning agents are notoriously sample inefficient, which considerably
limits their application to real-world problems. Recently, many model-based methods have …

Performing language-conditioned robotic manipulation tasks in unstructured environments
is highly demanded for general intelligent robots. Conventional robotic manipulation …