One practical challenge in reinforcement learning (RL) is how to make quick adaptations
when faced with new environments. In this paper, we propose a principled framework for …

While Reinforcement Learning has made great strides towards solving ever more
complicated tasks, many algorithms are still brittle to even slight changes in their …

Transfer and adaptation to new unknown environmental dynamics is a key challenge for
reinforcement learning (RL). An even greater challenge is performing near-optimally in a …

Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL)
by leveraging prior knowledge from past learned policies of relevant tasks. Existing transfer …

Designing agents that acquire knowledge autonomously and use it to solve new tasks
efficiently is an important challenge in reinforcement learning. Knowledge acquired during …

While current benchmark reinforcement learning (RL) tasks have been useful to drive
progress in the field, they are in many ways poor substitutes for learning with real-world …

Deep reinforcement learning agents have recently been successful across a variety of
discrete and continuous control tasks; however, they can be slow to train and require a large …

Reinforcement Learning methods are capable of solving complex problems, but resulting
policies might perform poorly in environments that are even slightly different. In robotics …

Abstract In Reinforcement Learning (RL), enhancing sample efficiency is crucial, particularly
in scenarios when data acquisition is costly and risky. In principle, off-policy RL algorithms …

In order to improve reproducibility, deep reinforcement learning (RL) has been adopting
better scientific practices such as standardized evaluation metrics and reporting. However …