Traditional computational approaches to design chemical species are limited by the need to
compute properties for a vast number of candidates, eg, by discriminative modeling …

The last half decade has seen a steep rise in the number of contributions on safe learning
methods for real-world robotic deployments from both the control and reinforcement learning …

Reinforcement learning from human feedback (RLHF) is a technique for training AI systems
to align with human goals. RLHF has emerged as the central method used to finetune state …

Large Language Models (LLMs) have excelled as high-level semantic planners for
sequential decision-making tasks. However, harnessing them to learn complex low-level …

Prompting has shown impressive success in enabling large pretrained language models
(LMs) to perform diverse NLP tasks, especially when only few downstream data are …

STABLE-BASELINES3 provides open-source implementations of deep reinforcement
learning (RL) algorithms in Python. The implementations have been benchmarked against …

Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing
their relative performance on a large suite of tasks. Most published results on deep RL …

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 …

Offline reinforcement learning (RL) defines the task of learning from a fixed batch of data.
Due to errors in value estimation from out-of-distribution actions, most offline RL algorithms …

To facilitate the personalized smart manufacturing paradigm with cognitive automation
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …