Optimization problems with nonlinear cost functions and combinatorial constraints appear in
many real-world applications but remain challenging to solve efficiently compared to their …

Planning-based reinforcement learning has shown strong performance in tasks in discrete
and low-dimensional continuous action spaces. However, planning usually brings …

In contrast to single-objective optimization (SOO), multi-objective optimization (MOO)
requires an optimizer to find the Pareto frontier, a subset of feasible solutions that are not …

We address the task of controlled generation of small molecules, which entails finding novel
molecules with desired properties under certain constraints (eg, similarity to a reference …

Structure-based Drug Design (SBDD), the task of designing 3D molecules (ligands) to bind
with a target protein pocket, is a fundamental task in drug discovery. Recent geometric deep …

Temporal abstraction and efficient planning pose significant challenges in offline
reinforcement learning, mainly when dealing with domains that involve temporally extended …

Bayesian optimization (BO) is a well-established methodology for optimizing costly black-
box functions. However, the sparse observations in the high-dimensional search space pose …

In recent times, molecular design has undergone significant advancements, particularly with
the integration of artificial intelligence (AI) techniques for discovering molecules with various …

Autonomous agents that collaborate with humans must understand language, track the state
of the world, and make good decisions. A central challenge common to these three …

The use of planning-based reinforcement learning is promising to solve tasks that require
long-term reasoning. It has been successful in completing tasks that involve actions in …