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 …

Reinforcement learning from human feedback (RLHF) can improve the quality of large
language model's (LLM) outputs by aligning them with human preferences. We propose a …

The grand aim of having a single robot that can manipulate arbitrary objects in diverse
settings is at odds with the paucity of robotics datasets. Acquiring and growing such datasets …

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

Collaborating with humans requires rapidly adapting to their individual strengths,
weaknesses, and preferences. Unfortunately, most standard multi-agent reinforcement …

Experience-related brain activity patterns reactivate during sleep, wakeful rest, and brief
pauses from active behavior. In parallel, machine learning research has found that …

Owing to their ability to both effectively integrate information over long time horizons and
scale to massive amounts of data, self-attention architectures have recently shown …

Abstract We introduce Phasic Policy Gradient (PPG), a reinforcement learning framework
which modifies traditional on-policy actor-critic methods by separating policy and value …

In recent years, reinforcement learning (RL) has been successfully applied to many different
continuous control tasks. While RL algorithms are often conceptually simple, their state-of …

We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven
exploration in visually complex environments. BYOL-Explore learns the world …