Building generally capable agents is a grand challenge for deep reinforcement learning
(RL). To approach this challenge practically, we outline two key desiderata: 1) to facilitate …

Safety is critical to broadening the application of reinforcement learning (RL). Often, we train
RL agents in a controlled environment, such as a laboratory, before deploying them in the …

Hierarchical model-based reinforcement learning (HMBRL) aims to combine the sample
efficiency of model-based reinforcement learning with the abstraction capability of …

Model-based reinforcement learning agents utilizing transformers have shown improved
sample efficiency due to their ability to model extended context, resulting in more accurate …

Safety is critical to broadening the application of reinforcement learning (RL). Often, RL
agents are trained in a controlled environment, such as a laboratory, before being deployed …

Safety has long been a crucial component in robotic systems, particularly in unstructured
environments where robustness and scalability pose serious challenges to various learning …

Autonomous agents are occupying more roles in our world than ever. They are present as AI
in games, decide on which ads users see on the internet, and are even considered in more …