Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …

Reinforcement learning has achieved great success in many applications. However, sample
efficiency remains a key challenge, with prominent methods requiring millions (or even …

Chemical reactions serve as foundational building blocks for organic chemistry and drug
design. In the era of large AI models, data-driven approaches have emerged to innovate the …

Abstract Model-based reinforcement learning approaches leverage a forward dynamics
model to support planning and decision making, which, however, may fail catastrophically if …

Many model-based reinforcement learning (RL) methods follow a similar template: fit a
model to previously observed data, and then use data from that model for RL or planning …

Dynamical systems that evolve continuously over time are ubiquitous throughout science
and engineering. Machine learning (ML) provides data-driven approaches to model and …

Designing and deriving effective model-based reinforcement learning (MBRL) algorithms
with a performance improvement guarantee is challenging, mainly attributed to the high …

Abstract Model-based reinforcement learning (RL) often achieves higher sample efficiency
in practice than model-free RL by learning a dynamics model to generate samples for policy …

We introduce the gamma-model, a predictive model of environment dynamics with an
infinite, probabilistic horizon. Replacing standard single-step models with gamma-models …

Accurately predicting the consequences of agents' actions is a key prerequisite for planning
in robotic control. Model-based reinforcement learning (MBRL) is one paradigm which relies …