Although deep reinforcement learning has become a promising machine learning approach
for sequential decision-making problems, it is still not mature enough for high-stake domains …

Reinforcement Learning from Human Feedback (RLHF) has been credited as the key
advance that has allowed Large Language Models (LLMs) to effectively follow instructions …

In recent years, reinforcement learning (RL) systems have shown impressive performance
and remarkable achievements. Many achievements can be attributed to combining RL with …

To improve the sustainability and resilience of modern food systems, designing improved
crop management strategies is crucial. The increasing abundance of data on agricultural …

The advancement of robots, particularly those functioning in complex human-centric
environments, relies on control solutions that are driven by machine learning …

Numerous real-world systems, ranging from healthcare to energy grids, involve users
competing for finite and potentially scarce resources. Designing policies for resource …

Medical decision making often relies on accurately forecasting future patient trajectories.
Conventional approaches for patient progression modeling often do not explicitly model …

In the field of eXplainable Artificial Intelligence (XAI), the generation of interpretable models
that are able to match the performance of state-of-the-art deep learning methods is one of …

Recently, we have witnessed the success of deep reinforcement learning (DRL) in many
security applications, ranging from malware mutation to selfish blockchain mining. Like all …

Tabular data remains one of the most prevalent data types across a wide range of real-world
applications, yet effective representation learning for this domain poses unique challenges …