A critical review of the existing Internal Combustion Engine (ICE) modeling, optimization,
diagnosis, and control challenges and the promising state-of-the-art Machine Learning (ML) …

Due to increasing complexity, uncertainty and data dimensions in power systems,
conventional methods often meet bottlenecks when attempting to solve decision and control …

Foundation models pretrained on diverse data at scale have demonstrated extraordinary
capabilities in a wide range of vision and language tasks. When such models are deployed …

Abstract Design of de novo biological sequences with desired properties, like protein and
DNA sequences, often involves an active loop with several rounds of molecule ideation and …

Generative Flow Networks (GFlowNets) have been introduced as a method to sample a
diverse set of candidates in an active learning context, with a training objective that makes …

Many modern approaches to offline Reinforcement Learning (RL) utilize behavior
regularization, typically augmenting a model-free actor critic algorithm with a penalty …

We propose a simple data augmentation technique that can be applied to standard model-
free reinforcement learning algorithms, enabling robust learning directly from pixels without …

Model-free deep reinforcement learning (RL) algorithms have been successfully applied to a
range of challenging sequential decision making and control tasks. However, these methods …

Generative flow networks (GFlowNets) are a method for learning a stochastic policy for
generating compositional objects, such as graphs or strings, from a given unnormalized …

Offline reinforcement learning (RL) aims to learn optimal policies from offline datasets,
where the parameterization of policies is crucial but often overlooked. Recently, Diffsuion-QL …