3 accepted papers
Continual learning aims to improve the ability of modern learning systems to deal with non-stationary distributions, typically by attempting to learn a series of tasks sequentially. Prior art in the field has largely considered supervised or reinforcement learning tasks, and often assumes full knowl…
We propose a general model-free deep reinforcement learning method and apply it to robotic manipulation tasks. Our approach leverages a small amount of demonstration data to assist a reinforcement learning agent. We train end-to-end visuomotor policies to learn a direct mapping from RGB camera input…
Domain adaptation is an important open problem in deep reinforcement learning (RL). In many scenarios of interest data is hard to obtain, so agents may learn a source policy in a setting where data is readily available, with the hope that it generalises well to the target domain. We propose a new mu…