ChatGPT, an artificial intelligence generated content (AIGC) model developed by OpenAI,
has attracted world-wide attention for its capability of dealing with challenging language …

Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of
indispensable factors to achieve human-level or super-human AI systems. On the other …

Many potential applications of artificial intelligence involve making real-time decisions in
physical systems while interacting with humans. Automobile racing represents an extreme …

To solve tasks in complex environments, robots need to learn from experience. Deep
reinforcement learning is a common approach to robot learning but requires a large amount …

Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …

Sample efficiency and exploration remain major challenges in online reinforcement learning
(RL). A powerful approach that can be applied to address these issues is the inclusion of …

Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization
from existing datasets followed by fast online fine-tuning with limited interaction. However …

This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a
tendency to rely on early interactions and ignore useful evidence encountered later …

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …