title: Data-efficient Deep Reinforcement Learning for Dexterous Manipulation
abstract: Grasping an object and precisely stacking it on another is a difficult task for traditional robotic control or hand-engineered approaches. Here we examine the problem in simulation and provide techniques aimed at solving it via deep reinforcement learning. We introduce two straightforward extensions to the Deep Deterministic Policy Gradient algorithm (DDPG), which make it significantly more data-efficient and scalable. Our results show that by making extensive use of off-policy data and replay, it is possible to find high-performance control policies. Further, our results hint that it may soon be feasible to train successful stacking policies by collecting interactions on real robots.
authors: Ivo Popov Nicolas Heess Timothy P. Lillicrap Roland Hafner Gabriel Barth-Maron Matej Vecerik Thomas Lampe Tom Erez Yuval Tassa Martin Riedmiller
hIndex: Timothy P. Lillicrap 58