10 citations · 50 across the 14 of their papers we have counts for
3 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…
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…
Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup
Devin Schwab, Tobias Springenberg, Murilo F. Martins +7
We present a method for fast training of vision based control policies on real robots. The key idea behind our method is to perform multi-task Reinforcement Learning with auxiliary…