Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …

The rise of deep learning has caused a paradigm shift in robotics research, favoring
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …

In offline reinforcement learning (RL), the goal is to learn a highly rewarding policy based
solely on a dataset of historical interactions with the environment. This serves as an extreme …

Offline reinforcement learning (RL) aims to find performant policies from logged data without
further environment interaction. Model-based algorithms, which learn a model of the …

Abstract We propose Adversarially Trained Actor Critic (ATAC), a new model-free algorithm
for offline reinforcement learning (RL) under insufficient data coverage, based on the …

Using a high Update-To-Data (UTD) ratio, model-based methods have recently achieved
much higher sample efficiency than previous model-free methods for continuous-action DRL …

Abstract We introduce Masked Trajectory Models (MTM) as a generic abstraction for
sequential decision making. MTM takes a trajectory, such as a state-action sequence, and …

The ability to autonomously learn behaviors via direct interactions in uninstrumented
environments can lead to generalist robots capable of enhancing productivity or providing …

In this review, the industry's current issues regarding intelligent manufacture are presented.
This work presents the status and the potential for the I4. 0 and I5. 0's revolutionary …

Most reinforcement learning (RL) algorithms assume online access to the environment, in
which one may readily interleave updates to the policy with experience collection using that …