With the rise of deep learning, there has been renewed interest within the process industries
to utilize data on large-scale nonlinear sensing and control problems. We identify key …

Multi-objective reinforcement learning (MORL) algorithms tackle sequential decision
problems where agents may have different preferences over (possibly conflicting) reward …

How to obtain good value estimation is a critical problem in Reinforcement Learning (RL).
Current value estimation methods in continuous control, such as DDPG and TD3, suffer from …

Diffusion models have become a popular choice for representing actor policies in behavior
cloning and offline reinforcement learning. This is due to their natural ability to optimize an …

Advanced model-based control methods have been widely used in industrial process
control, but excellent performance requires regular maintenance of its model. Reinforcement …

Sample efficiency is one of the most critical issues for online reinforcement learning (RL).
Existing methods achieve higher sample efficiency by adopting model-based methods, Q …

How sensitive should machine learning models be to input changes? We tackle the question
of model smoothness and show that it is a useful inductive bias which aids generalization …

Research in model-based reinforcement learning has made significant progress in recent
years. Compared to single-agent settings, the exponential dimension growth of the joint …

Recently, there are many efforts attempting to learn useful policies for continuous control in
visual reinforcement learning (RL). In this scenario, it is important to learn a generalizable …

Probabilistic dynamics model ensemble is widely used in existing model-based
reinforcement learning methods as it outperforms a single dynamics model in both …