Generalizing policies across different domains with dynamics mismatch poses a significant
challenge in reinforcement learning. For example, a robot learns the policy in a simulator …

The ability to reuse previous policies is an important aspect of human intelligence. To
achieve efficient policy reuse, a Deep Reinforcement Learning (DRL) agent needs to decide …

Residual reinforcement learning (RL) has been proposed as a way to solve challenging
robotic tasks by adapting control actions from a conventional feedback controller to …

The idea of transfer in reinforcement learning (TRL) is intriguing: being able to transfer
knowledge from one problem to another problem without learning everything from scratch …

Practical industrial assembly scenarios often require robotic agents to adapt their skills to
unseen tasks quickly. While transfer reinforcement learning (RL) could enable such quick …

We aim at creating a transfer reinforcement learning framework that allows the design of
learning controllers to leverage prior knowledge extracted from previously learned tasks and …

Emerging data-driven approaches, such as deep reinforcement learning (DRL), aim at on-
the-field learning of powertrain control policies that optimize fuel economy and other …

While classic control theory offers state of the art solutions in many problem scenarios, it is
often desired to improve beyond the structure of such solutions and surpass their limitations …

Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence.
In reinforcement learning, rationally reusing the policies acquired from other tasks or human …

Modern powertrain control design practices rely on model-based approaches accompanied
by costly calibrations in order to meet ever stringent energy use and emissions targets …