Offline reinforcement learning (RL) makes it possible to train the agents entirely from a
previously collected dataset. However, constrained by the quality of the offline dataset …

Recently, reward-conditioned reinforcement learning (RCRL) has gained popularity due to
its simplicity, flexibility, and off-policy nature. However, we will show that current RCRL …

Data is a critical asset in AI, as high-quality datasets can significantly improve the
performance of machine learning models. In safety-critical domains such as autonomous …

Training a deep neural network to maximize a target objective has become the standard
recipe for successful machine learning over the last decade. These networks can be …

Offline reinforcement learning (RL) has emerged as a promising paradigm for real-world
applications since it aims to train policies directly from datasets of past interactions with the …

This paper endeavors to augment the robustness of offline reinforcement learning (RL) in
scenarios laden with heavy-tailed rewards, a prevalent circumstance in real-world …

Making proper decision online in complex environment during the blast furnace (BF)
operation is a key factor in achieving long-term success and profitability in the steel …

Model-based reinforcement learning (RL) has shown great promise due to its sample
efficiency, but still struggles with long-horizon sparse-reward tasks, especially in offline …

Offline reinforcement learning (RL) offers a promising direction for learning policies from pre-
collected datasets without requiring further interactions with the environment. However …

In precision medicine, the ultimate goal is to recommend the most effective treatment to an
individual patient based on patient‐specific molecular and clinical profiles, possibly high …