284 citations · 822 across the 48 of their papers we have counts for
4 papers · 1 filter
Disentangled Cumulants Help Successor Representations Transfer to New Tasks
Christopher Grimm, Irina Higgins, Andre Barreto +5
Biological intelligence can learn to solve many diverse tasks in a data efficient manner by re-using basic knowledge and skills from one task to another. Furthermore, many of such…
Attention-Privileged Reinforcement Learning
Sasha Salter, Dushyant Rao, Markus Wulfmeier +2
Image-based Reinforcement Learning is known to suffer from poor sample efficiency and generalisation to unseen visuals such as distractors (task-independent aspects of the observat…
Compositional Transfer in Hierarchical Reinforcement Learning
Markus Wulfmeier, Abbas Abdolmaleki, Roland Hafner +7
The successful application of general reinforcement learning algorithms to real-world robotics applications is often limited by their high data requirements. We introduce Regulariz…
Efficient Supervision for Robot Learning via Imitation, Simulation, and Adaptation
Markus Wulfmeier
Recent successes in machine learning have led to a shift in the design of autonomous systems, improving performance on existing tasks and rendering new applications possible. Data-…